hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3b0fc358db5aac451f469b9295b1cfbd52a7a2e4
| 13,401
|
py
|
Python
|
scripts/xps_plot.py
|
nurblageij/periodic-patterns-mdl
|
e48a7011806f858ac14f0bfb8739cd6382ee01a6
|
[
"Apache-2.0"
] | 4
|
2019-03-05T03:24:53.000Z
|
2020-11-21T18:27:09.000Z
|
scripts/xps_plot.py
|
nurblageij/periodic-patterns-mdl
|
e48a7011806f858ac14f0bfb8739cd6382ee01a6
|
[
"Apache-2.0"
] | 1
|
2020-11-11T14:11:17.000Z
|
2020-11-11T14:51:23.000Z
|
scripts/xps_plot.py
|
nurblageij/periodic-patterns-mdl
|
e48a7011806f858ac14f0bfb8739cd6382ee01a6
|
[
"Apache-2.0"
] | 4
|
2019-07-07T21:05:07.000Z
|
2021-01-21T15:04:13.000Z
|
import numpy, re, sys
import matplotlib
import matplotlib.pyplot as plt
import pdb
SERIES = "vX"
if len(sys.argv) > 1:
SERIES = sys.argv[1]
XPS_REP = "../xps/"
FILE_IN = XPS_REP+"runs_results_%s.csv" % SERIES
FILE_IN_SACHA = XPS_REP+"runs_results_%s.csv" % SERIES
BASIS_OUT = XPS_REP+"fig"
CMAP_CMP = "rainbow"
CMAP_NBC = "binary"
CC = "S_nbOdup_max"
CX = "S_nb_cands"
inters = ["S", "V", "H", "V+H", "F"]
colors_all = ["r", "b", "g", "c", "k"]
colors_all = ["#332288", "#88CCEE", "#44AA99", "#117733", "#DDCC77", "#CC6677", "#AA4499", "#999933"]
colors_all = ["#332288", "#88CCEE", "#44AA99", "#DDCC77", "#CC6677"]
DT_NAMES_SHORT = {"3zap-0-rel": "3zap-0",
"3zap-1-rel": "3zap-1",
"bugzilla-0-rel-all": "bugzilla-0",
"bugzilla-1-rel-all": "bugzilla-1",
"sacha-18-absI-G1": "sacha-abs",
"sacha-18-abs": "sacha-abs",
"samba-auth-abs": "samba"}
DT_NAMES = {"sacha-18-rel": "sacha-rel"}
DT_NAMES.update(DT_NAMES_SHORT)
def getDName(name):
name = re.sub("-v[0-9]*$", "", re.sub("_", "-", name))
if name in DT_NAMES:
return DT_NAMES[name]
elif re.match("abs[I]?-G[0-9]+", name):
grain = name.split("G")[-1]
return "G%s" % grain
elif re.match("UbiqLog\-[0-9]+\-[FM]\-ISE\-abs", name):
which = "-".join(name.split("-")[1:3])
return "%s" % which
elif re.match("UbiqLog\-[0-9]+\-[FM]\-IS\-rel", name):
which = "-".join(name.split("-")[1:3])
return "%s" % which
return re.sub("-ISE?-[a-z]+$", "", re.sub("^UL-", "", name))
head = None
rlabels = []
with open(FILE_IN) as fp:
for line in fp:
if head is None:
head = line.strip().split()
else:
rlabels.append(line.split()[0])
# SUMMARY_TIME_PLOT
###############
if True:
map_field_num = dict([(v,k-1) for (k,v) in enumerate(head)])
X = numpy.loadtxt(FILE_IN, usecols=range(1, len(head)), skiprows=1)
Urids = [r for r in range(len(rlabels)) if re.match("Ubi.*abs", rlabels[r])]
rids = [r for r in range(len(rlabels)) if not re.match("Ubi", rlabels[r]) and (not re.match("sacha.*G", rlabels[r]) or re.match("sacha.*G15", rlabels[r])) and not re.search("_1_rel", rlabels[r])]
Arids = range(len(rlabels))
max_CX = numpy.max(X[Arids,map_field_num[CX]])
max_CC = 4.2 #numpy.log10(numpy.max(X[Arids,map_field_num[CC]]))
min_CC = .8 #numpy.log10(numpy.min(X[Arids,map_field_num[CC]]))
font = {'size': 22}
matplotlib.rc('font', **font)
msize = 120
mmsize = 60
sc_t = {"m": "min"}
for sc in ["m", "h"]:
plt.figure(figsize=(8,5))
# plt.scatter(X[Urids,map_field_num["size_O"]], X[Urids,map_field_num["runtime_mining"]], c=X[Urids,map_field_num["F_prc_cl"]], s=msize, marker='o', vmin=0, vmax=100, cmap=CMAP_CMP, zorder=20)
# plt.scatter(X[rids,map_field_num["size_O"]], X[rids,map_field_num["runtime_mining"]], c=X[rids,map_field_num["F_prc_cl"]], s=msize, marker="s", vmin=0, vmax=100, cmap=CMAP_CMP, zorder=20)
# plt.scatter(X[Urids,map_field_num["size_O"]], X[Urids,map_field_num["runtime_mining"]], c=numpy.log10(X[Urids,map_field_num[CC]]), s=mmsize+80*X[Urids,map_field_num[CX]]/max_CX, marker='o', cmap=CMAP_NBC, zorder=20, vmin=min_CC, vmax=max_CC)
# plt.scatter(X[rids,map_field_num["size_O"]], X[rids,map_field_num["runtime_mining"]], c=numpy.log10(X[rids,map_field_num[CC]]), s=mmsize+80*X[rids,map_field_num[CX]]/max_CX, marker='s', cmap=CMAP_NBC, zorder=20, vmin=min_CC, vmax=max_CC)
plt.plot(X[Urids,map_field_num["size_O"]], X[Urids,map_field_num["runtime_mining"]], "ko")
plt.plot(X[rids,map_field_num["size_O"]], X[rids,map_field_num["runtime_mining"]], "ko")
if sc == "h":
plt.plot([-1000, -1000, 45000, 45000],[-100, 3700, 3700, -100], "--", color="darkgray")
plt.xlim([-1000,195000])
plt.ylim([-100,10.5*3600])
ycmin, ycmax = plt.ylim()
plt.yticks(numpy.arange(0, ycmax, 3600), ["% 4d" %v for v in numpy.arange(0, ycmax/3600)])
else:
plt.xlim([-1000,45000])
plt.ylim([-100,3700])
ycmin, ycmax = plt.ylim()
plt.yticks(numpy.arange(0, ycmax, 600), ["% 4d" %v for v in numpy.arange(0, ycmax/60, 10)])
plt.ylabel("RT (%s)" % {"m": "min"}.get(sc, sc))
plt.xlabel("|S|")
plt.subplots_adjust(left=0.18, bottom=0.15, right=0.95, top=0.95)
plt.draw()
det = "all"
plt.savefig("%s_times(%s)_%s.pdf" % (BASIS_OUT, sc, det))
plt.clf()
###############
# DETAILED_TIME_PLOT
###############
if True:
map_field_num = dict([(v,k-1) for (k,v) in enumerate(head)])
X = numpy.loadtxt(FILE_IN, usecols=range(1, len(head)), skiprows=1)
Urids = [r for r in range(len(rlabels)) if re.match("Ubi", rlabels[r])]
rids = [r for r in range(len(rlabels)) if not re.match("Ubi", rlabels[r])]
Arids = range(len(rlabels))
max_CX = numpy.max(X[Arids,map_field_num[CX]])
max_CC = 4.2 #numpy.log10(numpy.max(X[Arids,map_field_num[CC]]))
min_CC = .8 #numpy.log10(numpy.min(X[Arids,map_field_num[CC]]))
font = {'size': 18}
matplotlib.rc('font', **font)
msize = 120
mmsize = 60
sc_t = {"m": "min"}
for sc in ["s", "m", "h", "l"]:
if sc == "l":
plt.figure(figsize=(9,6))
else:
plt.figure(figsize=(6,4))
for rid in range(len(rlabels)):
plt.plot([X[rid,map_field_num["size_O"]], X[rid,map_field_num["size_O"]]], [X[rid,map_field_num["runtime_combine"]], X[rid,map_field_num["runtime_mining"]]], ":k", zorder=5)
plt.scatter(X[Urids,map_field_num["size_O"]], X[Urids,map_field_num["runtime_mining"]], c=X[Urids,map_field_num["F_prc_cl"]], s=msize, marker='o', vmin=0, vmax=100, cmap=CMAP_CMP, zorder=20)
plt.scatter(X[rids,map_field_num["size_O"]], X[rids,map_field_num["runtime_mining"]], c=X[rids,map_field_num["F_prc_cl"]], s=msize, marker="s", vmin=0, vmax=100, cmap=CMAP_CMP, zorder=20)
if sc == "l":
cb = plt.colorbar(orientation="horizontal")
cb.set_label("$\%\mathit{L}$")
plt.scatter(X[Urids,map_field_num["size_O"]], X[Urids,map_field_num["runtime_combine"]], c=numpy.log10(X[Urids,map_field_num[CC]]), s=mmsize+80*X[Urids,map_field_num[CX]]/max_CX, marker='^', cmap=CMAP_NBC, zorder=10, vmin=min_CC, vmax=max_CC)
plt.scatter(X[rids,map_field_num["size_O"]], X[rids,map_field_num["runtime_combine"]], c=numpy.log10(X[rids,map_field_num[CC]]), s=mmsize+80*X[rids,map_field_num[CX]]/max_CX, marker="^", cmap=CMAP_NBC, zorder=10, vmin=min_CC, vmax=max_CC)
if sc == "l" :
# cb = plt.colorbar(orientation="vertical")
cb = plt.colorbar(orientation="horizontal")
cb.set_ticks([1,2,3,4])
cb.set_ticklabels(['$10$', '$10^2$', '$10^3$', '$10^4$'])
cb.set_label("$c^+$")
if sc == "h":
plt.plot([-1000, -1000, 22000, 22000],[-100, 1300, 1300, -100], "--", color="darkgray", zorder=30)
plt.plot([-1000, -1000, 45000, 45000],[-100, 3700, 3700, -100], "--", color="darkgray", zorder=30)
plt.xlim([-1000,195000])
plt.ylim([-100,10.5*3600])
ycmin, ycmax = plt.ylim()
plt.yticks(numpy.arange(0, ycmax, 3600), ["% 4d" %v for v in numpy.arange(0, ycmax/3600)])
elif sc == "m":
plt.plot([-1000, -1000, 22000, 22000],[-100, 1300, 1300, -100], "--", color="darkgray", zorder=30)
plt.xlim([-1000,45000])
plt.ylim([-100,3700])
ycmin, ycmax = plt.ylim()
plt.yticks(numpy.arange(0, ycmax, 600), ["% 4d" %v for v in numpy.arange(0, ycmax/60, 10)])
else:
plt.xlim([-1000,22000])
plt.ylim([-100,1300])
if sc != "l":
plt.ylabel("RT (%s)" % {"m": "min"}.get(sc, sc))
plt.xlabel("|S|")
plt.subplots_adjust(left=0.18, bottom=0.15, right=0.95, top=0.95)
plt.draw()
det = "details"
plt.savefig("%s_times(%s)_%s.pdf" % (BASIS_OUT, sc, det))
plt.clf()
###############
ucols = [ci for ci in range(len(head)) if re.search("prc_cl", head[ci])]
map_inter_to_cid = dict([(v,k) for (k,v) in enumerate([head[ci].split("_")[0] for ci in ucols])])
Xorg = numpy.loadtxt(FILE_IN, usecols=ucols, skiprows=1)
scols = [ci for ci in range(len(head)) if re.search("size_O", head[ci])]
Sorg = numpy.loadtxt(FILE_IN, usecols=scols, skiprows=1)
bar_labels = [re.sub("_prc_cl", "", head[ui]) for ui in ucols]
groups = []
rids = [r for r in range(len(rlabels)) if re.match("Ubi.*ISE", rlabels[r])]
rids.sort(key=lambda x: -Sorg[x])
groups.append({"title": "UbiqLog ISE Abs", "inter": ["S", "F"], "rids": rids,
"rlabels": [rlabels[r] for r in rids]})
groups.append({"title": "UbiqLog ISE Abs 2-2", "inter": ["S", "F"], "rids": rids[len(rids)/2:],
"rlabels": [rlabels[r] for r in rids[len(rids)/2:]]})
groups.append({"title": "UbiqLog ISE Abs 1-2", "inter": ["S", "F"], "rids": rids[:len(rids)/2],
"rlabels": [rlabels[r] for r in rids[:len(rids)/2]]})
rids = [r for r in range(len(rlabels)) if re.match("Ubi.*IS_rel", rlabels[r])]
rids.sort(key=lambda x: -Sorg[x])
groups.append({"title": "UbiqLog IS Rel", "inter": ["S", "F"], "rids": rids,
"rlabels": [rlabels[r] for r in rids]})
rids = [r for r in range(len(rlabels)) if not re.match("Ubi.*", rlabels[r]) and not re.match("sacha", rlabels[r])][::-1]
groups.append({"title": "Other", "rids": rids,
"rlabels": [rlabels[r] for r in rids]})
for group in groups:
font = {'size' : 12}
matplotlib.rc('font', **font)
if re.search("-2$", group["title"]):
font = {'size' : 20}
matplotlib.rc('font', **font)
X = Xorg[group["rids"], :]
colors = colors_all
blabels = bar_labels
if "inter" in group:
cids = [map_inter_to_cid[inter] for inter in group["inter"]]
else:
cids = [map_inter_to_cid[inter] for inter in inters]
X = X[:,cids]
colors = [colors_all[c] for c in cids]
blabels = ["$\\mathcal{C}_{%s}$" % bar_labels[c] for c in cids]
rlabels = ["%s" % getDName(rll) for rll in group["rlabels"]]
plt.figure(figsize=(8,5))
for i in range(X.shape[1]):
plt.barh(numpy.arange(X.shape[0])+i*.8/X.shape[1]-.8/2, X[:,i], .8*.8/X.shape[1], color=colors[i])
plt.barh(-2, 1, 1, color=colors[i], label=blabels[i])
if re.search("-2$", group["title"]):
plt.yticks(range(len(group["rlabels"])), rlabels, fontsize=18)
elif "Other" == group["title"]:
plt.yticks(range(len(group["rlabels"])), rlabels, fontsize=12)
else:
plt.yticks(range(len(group["rlabels"])), rlabels, fontsize=10)
plt.xlabel("$\\%\\mathit{L}$")
plt.xlim([0,100])
plt.ylim([-1,X.shape[0]])
if re.search("2-2", group["title"]) is None:
plt.legend(loc=4,frameon=False)
if re.search("-2$", group["title"]):
plt.subplots_adjust(bottom=0.14, top=.92)
plt.draw()
plt.savefig("%s_prcCL_%s.pdf" % (BASIS_OUT, re.sub(" ", "", group["title"])))
####################### SACHA
head = None
rlabels = []
with open(FILE_IN_SACHA) as fp:
for line in fp:
if head is None:
head = line.strip().split()
else:
rlabels.append(line.split()[0])
ucols = [ci for ci in range(len(head)) if re.search("prc_cl", head[ci])]
map_inter_to_cid = dict([(v,k) for (k,v) in enumerate([head[ci].split("_")[0] for ci in ucols])])
Xorg = numpy.loadtxt(FILE_IN_SACHA, usecols=ucols, skiprows=1)
bar_labels = [re.sub("_prc_cl", "", head[ui]) for ui in ucols]
groups = []
rids = [r for r in range(len(rlabels)) if re.match("sacha", rlabels[r])]
skeys = dict([(rid, (int((rlabels[rid]+"G9999").split("G")[1].split("_")[0]), "2000" not in rlabels[rid], "absI" not in rlabels[rid])) for rid in rids])
rids.sort(key=lambda x: skeys[x], reverse=True)
groups.append({"title": "Sacha", "inter": ["S", "F"], "rids": rids,
"rlabels": [re.sub("2000", "S", re.sub("_v[0-9]*", "", re.sub("sacha_18_", "", rlabels[r]))) for r in rids]})
font = {'size' : 12}
matplotlib.rc('font', **font)
for group in groups:
X = Xorg[group["rids"], :]
colors = colors_all
blabels = bar_labels
if "inter" in group:
cids = [map_inter_to_cid[inter] for inter in group["inter"]]
else:
cids = [map_inter_to_cid[inter] for inter in inters]
X = X[:,cids]
colors = [colors_all[c] for c in cids]
blabels = ["$\\mathcal{C}_{%s}$" % bar_labels[c] for c in cids]
rlabels = ["%s" % getDName(rll) for rll in group["rlabels"]]
plt.figure(figsize=(8,2))
for i in range(X.shape[1]):
plt.barh(numpy.arange(X.shape[0])+i*.8/X.shape[1]-.8/2, X[:,i], .8*.8/X.shape[1], color=colors[i])
plt.barh(-2, 1, 1, color=colors[i], label=blabels[i])
plt.yticks(range(len(group["rlabels"])), rlabels, fontsize=12)
plt.xlabel("$\\%\\mathit{L}$")
plt.xlim([0,100])
plt.ylim([-1,X.shape[0]])
plt.legend(loc=4,frameon=False)
plt.subplots_adjust(bottom=0.32, top=0.95)
plt.draw()
plt.savefig("%s_prcCL_%s.pdf" % (BASIS_OUT, re.sub(" ", "", group["title"])))
| 42.814696
| 251
| 0.573614
| 2,125
| 13,401
| 3.496471
| 0.118118
| 0.047376
| 0.065141
| 0.030148
| 0.831225
| 0.801211
| 0.772948
| 0.719246
| 0.712248
| 0.699462
| 0
| 0.055123
| 0.205358
| 13,401
| 312
| 252
| 42.951923
| 0.642596
| 0.085889
| 0
| 0.604082
| 0
| 0
| 0.123653
| 0.005019
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.016327
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3b8c6a199f7cf62db1fa6cbde957db0eb047ffb3
| 227
|
py
|
Python
|
pds4_tools/__init__.py
|
LandingEllipse/pds4_tools
|
8ada764ae1ae102d1a17ac4820cb799b87d7041a
|
[
"BSD-3-Clause"
] | null | null | null |
pds4_tools/__init__.py
|
LandingEllipse/pds4_tools
|
8ada764ae1ae102d1a17ac4820cb799b87d7041a
|
[
"BSD-3-Clause"
] | null | null | null |
pds4_tools/__init__.py
|
LandingEllipse/pds4_tools
|
8ada764ae1ae102d1a17ac4820cb799b87d7041a
|
[
"BSD-3-Clause"
] | null | null | null |
from pds4_tools.__about__ import (__version__, __author__, __email__, __copyright__)
from .reader import pds4_read
from .viewer import pds4_viewer
from .reader import pds4_read as read
from .viewer import pds4_viewer as view
| 28.375
| 84
| 0.828194
| 33
| 227
| 4.939394
| 0.424242
| 0.245399
| 0.196319
| 0.245399
| 0.638037
| 0.368098
| 0
| 0
| 0
| 0
| 0
| 0.025253
| 0.127753
| 227
| 7
| 85
| 32.428571
| 0.79798
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
8e5a11a909cc10695d6e2bb56e4dee75e1e944fe
| 85
|
py
|
Python
|
archstart/hello.py
|
jacobjhansen/archstart
|
959301b71dbbe534c736e133dcd60199494819af
|
[
"MIT"
] | null | null | null |
archstart/hello.py
|
jacobjhansen/archstart
|
959301b71dbbe534c736e133dcd60199494819af
|
[
"MIT"
] | null | null | null |
archstart/hello.py
|
jacobjhansen/archstart
|
959301b71dbbe534c736e133dcd60199494819af
|
[
"MIT"
] | null | null | null |
import getpass
def say_hello():
print("Hello, {} =)".format(getpass.getuser()))
| 17
| 51
| 0.647059
| 10
| 85
| 5.4
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141176
| 85
| 4
| 52
| 21.25
| 0.739726
| 0
| 0
| 0
| 0
| 0
| 0.141176
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0.666667
| 0.333333
| 0
| 0.666667
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
8ed1ab39b6acc47511edc585fe0749cb940aefa8
| 36
|
py
|
Python
|
python_in_action/modules/sound/formats/__init__.py
|
wang-junjian/learn-python
|
078a260f023b7bd7083132baea7ec0c09d6a2bef
|
[
"MIT"
] | null | null | null |
python_in_action/modules/sound/formats/__init__.py
|
wang-junjian/learn-python
|
078a260f023b7bd7083132baea7ec0c09d6a2bef
|
[
"MIT"
] | null | null | null |
python_in_action/modules/sound/formats/__init__.py
|
wang-junjian/learn-python
|
078a260f023b7bd7083132baea7ec0c09d6a2bef
|
[
"MIT"
] | null | null | null |
print('sound/formats/__init__.py')
| 12
| 34
| 0.75
| 5
| 36
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.055556
| 36
| 2
| 35
| 18
| 0.676471
| 0
| 0
| 0
| 0
| 0
| 0.714286
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
d98102368fdd40b7cb361dfbb62405931b1cada6
| 19,180
|
py
|
Python
|
sdk/python/pulumi_azure/storage/table_entity.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 109
|
2018-06-18T00:19:44.000Z
|
2022-02-20T05:32:57.000Z
|
sdk/python/pulumi_azure/storage/table_entity.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 663
|
2018-06-18T21:08:46.000Z
|
2022-03-31T20:10:11.000Z
|
sdk/python/pulumi_azure/storage/table_entity.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 41
|
2018-07-19T22:37:38.000Z
|
2022-03-14T10:56:26.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['TableEntityArgs', 'TableEntity']
@pulumi.input_type
class TableEntityArgs:
def __init__(__self__, *,
entity: pulumi.Input[Mapping[str, pulumi.Input[str]]],
partition_key: pulumi.Input[str],
row_key: pulumi.Input[str],
storage_account_name: pulumi.Input[str],
table_name: pulumi.Input[str]):
"""
The set of arguments for constructing a TableEntity resource.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] entity: A map of key/value pairs that describe the entity to be inserted/merged in to the storage table.
:param pulumi.Input[str] partition_key: The key for the partition where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] row_key: The key for the row where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] storage_account_name: Specifies the storage account in which to create the storage table entity.
Changing this forces a new resource to be created.
:param pulumi.Input[str] table_name: The name of the storage table in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
pulumi.set(__self__, "entity", entity)
pulumi.set(__self__, "partition_key", partition_key)
pulumi.set(__self__, "row_key", row_key)
pulumi.set(__self__, "storage_account_name", storage_account_name)
pulumi.set(__self__, "table_name", table_name)
@property
@pulumi.getter
def entity(self) -> pulumi.Input[Mapping[str, pulumi.Input[str]]]:
"""
A map of key/value pairs that describe the entity to be inserted/merged in to the storage table.
"""
return pulumi.get(self, "entity")
@entity.setter
def entity(self, value: pulumi.Input[Mapping[str, pulumi.Input[str]]]):
pulumi.set(self, "entity", value)
@property
@pulumi.getter(name="partitionKey")
def partition_key(self) -> pulumi.Input[str]:
"""
The key for the partition where the entity will be inserted/merged. Changing this forces a new resource.
"""
return pulumi.get(self, "partition_key")
@partition_key.setter
def partition_key(self, value: pulumi.Input[str]):
pulumi.set(self, "partition_key", value)
@property
@pulumi.getter(name="rowKey")
def row_key(self) -> pulumi.Input[str]:
"""
The key for the row where the entity will be inserted/merged. Changing this forces a new resource.
"""
return pulumi.get(self, "row_key")
@row_key.setter
def row_key(self, value: pulumi.Input[str]):
pulumi.set(self, "row_key", value)
@property
@pulumi.getter(name="storageAccountName")
def storage_account_name(self) -> pulumi.Input[str]:
"""
Specifies the storage account in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
return pulumi.get(self, "storage_account_name")
@storage_account_name.setter
def storage_account_name(self, value: pulumi.Input[str]):
pulumi.set(self, "storage_account_name", value)
@property
@pulumi.getter(name="tableName")
def table_name(self) -> pulumi.Input[str]:
"""
The name of the storage table in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
return pulumi.get(self, "table_name")
@table_name.setter
def table_name(self, value: pulumi.Input[str]):
pulumi.set(self, "table_name", value)
@pulumi.input_type
class _TableEntityState:
def __init__(__self__, *,
entity: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
partition_key: Optional[pulumi.Input[str]] = None,
row_key: Optional[pulumi.Input[str]] = None,
storage_account_name: Optional[pulumi.Input[str]] = None,
table_name: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering TableEntity resources.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] entity: A map of key/value pairs that describe the entity to be inserted/merged in to the storage table.
:param pulumi.Input[str] partition_key: The key for the partition where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] row_key: The key for the row where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] storage_account_name: Specifies the storage account in which to create the storage table entity.
Changing this forces a new resource to be created.
:param pulumi.Input[str] table_name: The name of the storage table in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
if entity is not None:
pulumi.set(__self__, "entity", entity)
if partition_key is not None:
pulumi.set(__self__, "partition_key", partition_key)
if row_key is not None:
pulumi.set(__self__, "row_key", row_key)
if storage_account_name is not None:
pulumi.set(__self__, "storage_account_name", storage_account_name)
if table_name is not None:
pulumi.set(__self__, "table_name", table_name)
@property
@pulumi.getter
def entity(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
A map of key/value pairs that describe the entity to be inserted/merged in to the storage table.
"""
return pulumi.get(self, "entity")
@entity.setter
def entity(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "entity", value)
@property
@pulumi.getter(name="partitionKey")
def partition_key(self) -> Optional[pulumi.Input[str]]:
"""
The key for the partition where the entity will be inserted/merged. Changing this forces a new resource.
"""
return pulumi.get(self, "partition_key")
@partition_key.setter
def partition_key(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "partition_key", value)
@property
@pulumi.getter(name="rowKey")
def row_key(self) -> Optional[pulumi.Input[str]]:
"""
The key for the row where the entity will be inserted/merged. Changing this forces a new resource.
"""
return pulumi.get(self, "row_key")
@row_key.setter
def row_key(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "row_key", value)
@property
@pulumi.getter(name="storageAccountName")
def storage_account_name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the storage account in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
return pulumi.get(self, "storage_account_name")
@storage_account_name.setter
def storage_account_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "storage_account_name", value)
@property
@pulumi.getter(name="tableName")
def table_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the storage table in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
return pulumi.get(self, "table_name")
@table_name.setter
def table_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "table_name", value)
class TableEntity(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
entity: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
partition_key: Optional[pulumi.Input[str]] = None,
row_key: Optional[pulumi.Input[str]] = None,
storage_account_name: Optional[pulumi.Input[str]] = None,
table_name: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Manages an Entity within a Table in an Azure Storage Account.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_account = azure.storage.Account("exampleAccount",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
account_tier="Standard",
account_replication_type="LRS")
example_table = azure.storage.Table("exampleTable", storage_account_name=example_account.name)
example_table_entity = azure.storage.TableEntity("exampleTableEntity",
storage_account_name=example_account.name,
table_name=example_table.name,
partition_key="examplepartition",
row_key="examplerow",
entity={
"example": "example",
})
```
## Import
Entities within a Table in an Azure Storage Account can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:storage/tableEntity:TableEntity entity1 https://example.table.core.windows.net/table1(PartitionKey='samplepartition',RowKey='samplerow')
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] entity: A map of key/value pairs that describe the entity to be inserted/merged in to the storage table.
:param pulumi.Input[str] partition_key: The key for the partition where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] row_key: The key for the row where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] storage_account_name: Specifies the storage account in which to create the storage table entity.
Changing this forces a new resource to be created.
:param pulumi.Input[str] table_name: The name of the storage table in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: TableEntityArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Manages an Entity within a Table in an Azure Storage Account.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_account = azure.storage.Account("exampleAccount",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
account_tier="Standard",
account_replication_type="LRS")
example_table = azure.storage.Table("exampleTable", storage_account_name=example_account.name)
example_table_entity = azure.storage.TableEntity("exampleTableEntity",
storage_account_name=example_account.name,
table_name=example_table.name,
partition_key="examplepartition",
row_key="examplerow",
entity={
"example": "example",
})
```
## Import
Entities within a Table in an Azure Storage Account can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:storage/tableEntity:TableEntity entity1 https://example.table.core.windows.net/table1(PartitionKey='samplepartition',RowKey='samplerow')
```
:param str resource_name: The name of the resource.
:param TableEntityArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(TableEntityArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
entity: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
partition_key: Optional[pulumi.Input[str]] = None,
row_key: Optional[pulumi.Input[str]] = None,
storage_account_name: Optional[pulumi.Input[str]] = None,
table_name: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = TableEntityArgs.__new__(TableEntityArgs)
if entity is None and not opts.urn:
raise TypeError("Missing required property 'entity'")
__props__.__dict__["entity"] = entity
if partition_key is None and not opts.urn:
raise TypeError("Missing required property 'partition_key'")
__props__.__dict__["partition_key"] = partition_key
if row_key is None and not opts.urn:
raise TypeError("Missing required property 'row_key'")
__props__.__dict__["row_key"] = row_key
if storage_account_name is None and not opts.urn:
raise TypeError("Missing required property 'storage_account_name'")
__props__.__dict__["storage_account_name"] = storage_account_name
if table_name is None and not opts.urn:
raise TypeError("Missing required property 'table_name'")
__props__.__dict__["table_name"] = table_name
super(TableEntity, __self__).__init__(
'azure:storage/tableEntity:TableEntity',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
entity: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
partition_key: Optional[pulumi.Input[str]] = None,
row_key: Optional[pulumi.Input[str]] = None,
storage_account_name: Optional[pulumi.Input[str]] = None,
table_name: Optional[pulumi.Input[str]] = None) -> 'TableEntity':
"""
Get an existing TableEntity resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] entity: A map of key/value pairs that describe the entity to be inserted/merged in to the storage table.
:param pulumi.Input[str] partition_key: The key for the partition where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] row_key: The key for the row where the entity will be inserted/merged. Changing this forces a new resource.
:param pulumi.Input[str] storage_account_name: Specifies the storage account in which to create the storage table entity.
Changing this forces a new resource to be created.
:param pulumi.Input[str] table_name: The name of the storage table in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _TableEntityState.__new__(_TableEntityState)
__props__.__dict__["entity"] = entity
__props__.__dict__["partition_key"] = partition_key
__props__.__dict__["row_key"] = row_key
__props__.__dict__["storage_account_name"] = storage_account_name
__props__.__dict__["table_name"] = table_name
return TableEntity(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def entity(self) -> pulumi.Output[Mapping[str, str]]:
"""
A map of key/value pairs that describe the entity to be inserted/merged in to the storage table.
"""
return pulumi.get(self, "entity")
@property
@pulumi.getter(name="partitionKey")
def partition_key(self) -> pulumi.Output[str]:
"""
The key for the partition where the entity will be inserted/merged. Changing this forces a new resource.
"""
return pulumi.get(self, "partition_key")
@property
@pulumi.getter(name="rowKey")
def row_key(self) -> pulumi.Output[str]:
"""
The key for the row where the entity will be inserted/merged. Changing this forces a new resource.
"""
return pulumi.get(self, "row_key")
@property
@pulumi.getter(name="storageAccountName")
def storage_account_name(self) -> pulumi.Output[str]:
"""
Specifies the storage account in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
return pulumi.get(self, "storage_account_name")
@property
@pulumi.getter(name="tableName")
def table_name(self) -> pulumi.Output[str]:
"""
The name of the storage table in which to create the storage table entity.
Changing this forces a new resource to be created.
"""
return pulumi.get(self, "table_name")
| 45.995204
| 167
| 0.655683
| 2,376
| 19,180
| 5.092172
| 0.077441
| 0.074552
| 0.077527
| 0.043971
| 0.861972
| 0.841392
| 0.828581
| 0.798165
| 0.782957
| 0.767171
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| 0.252607
| 19,180
| 416
| 168
| 46.105769
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0
| 7
|
797d298549d8a5cef7ece4d32b768d73b558035a
| 2,086
|
py
|
Python
|
modules/api_gateway.py
|
mmarukaw/oci-bulk-operations
|
9dc5434ddbfcb80abaf250036d752e4d22e1c00b
|
[
"MIT"
] | 1
|
2021-03-22T02:59:19.000Z
|
2021-03-22T02:59:19.000Z
|
modules/api_gateway.py
|
mmarukaw/oci-bulk-operations
|
9dc5434ddbfcb80abaf250036d752e4d22e1c00b
|
[
"MIT"
] | null | null | null |
modules/api_gateway.py
|
mmarukaw/oci-bulk-operations
|
9dc5434ddbfcb80abaf250036d752e4d22e1c00b
|
[
"MIT"
] | null | null | null |
import oci
from modules.common import *
client_gw = oci.apigateway.GatewayClient
client_dp = oci.apigateway.DeploymentClient
def purge_deployments(config, signer, compartments):
target = TargetResources()
target.resource_names = ['api deployments']
target.action = 'DELETE'
target.target_state = 'DELETED'
target.state_in_action = 'DELETING'
target.list_methods = [client_dp(config, signer=signer).list_deployments]
target.list_args = [None]
target.dispname_keys = ['display_name']
target.parentid_keys = [None]
target.get_method = client_dp(config, signer=signer).get_deployment
target.action_method = client_dp(config, signer=signer).delete_deployment
target.action_args = {}
def filter_logic(resource):
if (resource.lifecycle_state not in ['DELETING', 'DELETED']):
return True
else:
return False
target.filter_logics = [filter_logic]
target_resources = target.list(compartments)
target.commit_action(target_resources)
target.wait_completion(target_resources)
def purge_gateways(config, signer, compartments):
target = TargetResources()
target.resource_names = ['api gateways']
target.action = 'DELETE'
target.target_state = 'DELETED'
target.state_in_action = 'DELETING'
target.list_methods = [client_gw(config, signer=signer).list_gateways]
target.list_args = [None]
target.dispname_keys = ['display_name']
target.parentid_keys = [None]
target.get_method = client_gw(config, signer=signer).get_gateway
target.action_method = client_gw(config, signer=signer).delete_gateway
target.action_args = {}
def filter_logic(resource):
if (resource.lifecycle_state not in ['DELETING', 'DELETED']):
return True
else:
return False
target.filter_logics = [filter_logic]
target_resources = target.list(compartments)
target.commit_action(target_resources)
target.wait_completion(target_resources)
| 36.596491
| 80
| 0.691275
| 232
| 2,086
| 5.965517
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| 0.812139
| 0.783237
| 0.708092
| 0.708092
| 0.708092
| 0.611272
| 0
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| 0.212848
| 2,086
| 56
| 81
| 37.25
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| false
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| null | 0
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| 1
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| 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
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| null | 0
| 0
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| 0
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| 0
| 0
| 0
|
0
| 7
|
7989a510652a680d8118a766adfc2f55aaad73f3
| 42
|
py
|
Python
|
cipher_ljt2138/tests/test_cipher_ljt2138.py
|
QMSS-G5072-2021/cipher_tan_lash
|
8cfadd7a1a15e8f074513e04301b5be7d9a2e97f
|
[
"MIT"
] | null | null | null |
cipher_ljt2138/tests/test_cipher_ljt2138.py
|
QMSS-G5072-2021/cipher_tan_lash
|
8cfadd7a1a15e8f074513e04301b5be7d9a2e97f
|
[
"MIT"
] | null | null | null |
cipher_ljt2138/tests/test_cipher_ljt2138.py
|
QMSS-G5072-2021/cipher_tan_lash
|
8cfadd7a1a15e8f074513e04301b5be7d9a2e97f
|
[
"MIT"
] | null | null | null |
from cipher_ljt2138 import cipher_ljt2138
| 21
| 41
| 0.904762
| 6
| 42
| 6
| 0.666667
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.210526
| 0.095238
| 42
| 1
| 42
| 42
| 0.736842
| 0
| 0
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| 0
| 0
| 0
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| true
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| null | 1
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| null | 0
| 0
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| 0
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
798ddd7de669c41eca2a38704e3d49ca98154025
| 103,811
|
py
|
Python
|
TIDALDL-PY/tidal_gui/resource/images.py
|
PatrykMis/Tidal-Media-Downloader
|
2484a8d4d8460429c4064ab5165e9c8f95f5abe3
|
[
"Apache-2.0"
] | null | null | null |
TIDALDL-PY/tidal_gui/resource/images.py
|
PatrykMis/Tidal-Media-Downloader
|
2484a8d4d8460429c4064ab5165e9c8f95f5abe3
|
[
"Apache-2.0"
] | null | null | null |
TIDALDL-PY/tidal_gui/resource/images.py
|
PatrykMis/Tidal-Media-Downloader
|
2484a8d4d8460429c4064ab5165e9c8f95f5abe3
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_resource_struct_v2 = b"\
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qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
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| 129
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| false
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0
| 10
|
5c0ac88ef9299163cc9d1eb3de44d04695873238
| 111
|
py
|
Python
|
src/ribo_api/models/__init__.py
|
RinPham/RiBo-Core
|
8c5a00a215b42aad2f6a4167b9cb97fe11d78823
|
[
"MIT"
] | null | null | null |
src/ribo_api/models/__init__.py
|
RinPham/RiBo-Core
|
8c5a00a215b42aad2f6a4167b9cb97fe11d78823
|
[
"MIT"
] | null | null | null |
src/ribo_api/models/__init__.py
|
RinPham/RiBo-Core
|
8c5a00a215b42aad2f6a4167b9cb97fe11d78823
|
[
"MIT"
] | null | null | null |
from ribo_api.models.user import User
from ribo_api.models.task import Task
from ribo_api.models.api import Api
| 37
| 37
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| 111
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0
| 8
|
3092cfdce8199703b4919db91940bd7baf2496bc
| 105,350
|
py
|
Python
|
remodet_repository_wdh_part/Projects/DetPoseJoint/mPoseNet_DarkNet.py
|
UrwLee/Remo_experience
|
a59d5b9d6d009524672e415c77d056bc9dd88c72
|
[
"MIT"
] | null | null | null |
remodet_repository_wdh_part/Projects/DetPoseJoint/mPoseNet_DarkNet.py
|
UrwLee/Remo_experience
|
a59d5b9d6d009524672e415c77d056bc9dd88c72
|
[
"MIT"
] | null | null | null |
remodet_repository_wdh_part/Projects/DetPoseJoint/mPoseNet_DarkNet.py
|
UrwLee/Remo_experience
|
a59d5b9d6d009524672e415c77d056bc9dd88c72
|
[
"MIT"
] | null | null | null |
import caffe
from caffe import layers as L
from caffe import params as P
from mPoseNet_Reduce import mPose_StageX_Train
from mPoseBaseNet import *
from PyLib.NetLib.MultiScaleLayer import *
from PyLib.NetLib.ConvBNLayer import *
############################################################
# Compress the YoloNet.
# Remove all the pool layers.
# Change the stride of the original conv layers before pool layers to 2.
############################################################
def YoloNetPartBigStride(net, from_layer="data", use_bn=True, use_sub_layers=(1,1,3,3,5), leaky=False,
num_channels = (32,64,128,256,512),kernel_sizes = (7,3,3,3,3),strides = (4,2,2,1,1),deconv_channels = [256,128],
strides_last_flags = (True,True,True,True,True),pooling_flags = (False,)*5,ChangeNameAndChannel = {},lr=1, decay=1, addstrs = ""):
assert len(use_sub_layers) == len(num_channels) == len(kernel_sizes) == len(strides) == len(strides_last_flags)
for layer in xrange(len(num_channels)):
for sub_layer in xrange(use_sub_layers[layer]):
if use_sub_layers[layer] == 1:
out_layer = "conv{}".format(layer + 1)
else:
out_layer = "conv{}_{}".format(layer + 1, sub_layer + 1)
if strides_last_flags[layer]:
if sub_layer == use_sub_layers[layer] - 1:
stride_i = strides[layer]
else:
stride_i = 1
else:
if sub_layer == 0:
stride_i = strides[layer]
else:
stride_i = 1
if sub_layer%2 == 0:
kernel_size_i = kernel_sizes[layer]
num_channel_i = num_channels[layer]
else:
kernel_size_i = 1
num_channel_i = num_channels[layer]/2
if out_layer in ChangeNameAndChannel.keys():
if ChangeNameAndChannel[out_layer] != 0:
num_channel_i = ChangeNameAndChannel[out_layer]
out_layer += "_new"
out_layer += addstrs
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=use_bn, use_relu=True,num_output=num_channel_i,
kernel_size=kernel_size_i, pad=(kernel_size_i-1)/2, stride=stride_i,
use_scale=True, leaky=leaky,lr_mult=lr,decay_mult=decay)
if pooling_flags[layer]:
out_layer = 'pool{}'.format(layer+1)
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX,
kernel_size=2, stride=2, pad=0)
from_layer = out_layer
else:
from_layer = out_layer
deconv_param = {
'num_output': deconv_channels[0],
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_deconv = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': deconv_param
}
bn_kwargs = {
'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)],
'eps': 0.001,
}
sb_kwargs = {
'bias_term': True,
'param': [dict(lr_mult=1, decay_mult=0), dict(lr_mult=1, decay_mult=0)],
'filler': dict(type='constant', value=1.0),
'bias_filler': dict(type='constant', value=0.2),
}
from_layer = "conv4_{}".format(use_sub_layers[-2]) + addstrs
add_layer = from_layer + "_deconv"
print from_layer, add_layer
net[add_layer] = L.Deconvolution(net[from_layer], **kwargs_deconv)
bn_name = add_layer + '_bn'
net[bn_name] = L.BatchNorm(net[add_layer], in_place=True, **bn_kwargs)
sb_name = add_layer + '_scale'
net[sb_name] = L.Scale(net[add_layer], in_place=True, **sb_kwargs)
relu_name = add_layer + '_relu'
net[relu_name] = L.ReLU(net[add_layer], in_place=True)
deconv_param1 = {
'num_output': deconv_channels[1],
'kernel_size': 4,
'pad': 0,
'stride': 4,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_deconv1 = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': deconv_param1
}
from_layer = "conv5_{}".format(use_sub_layers[-1]) + addstrs
add_layer = from_layer + "_deconv"
net[add_layer] = L.Deconvolution(net[from_layer], **kwargs_deconv1)
bn_name = add_layer + '_bn'
net[bn_name] = L.BatchNorm(net[add_layer], in_place=True, **bn_kwargs)
sb_name = add_layer + '_scale'
net[sb_name] = L.Scale(net[add_layer], in_place=True, **sb_kwargs)
relu_name = add_layer + '_relu'
net[relu_name] = L.ReLU(net[add_layer], in_place=True)
return net
def Flexible_Base(net, from_layer="data", use_bn=True, leaky=False,
num_channels = ((32,),(64,),(64,32,256),(256,64,256,64,256,128,128)),
kernel_sizes = ((7,),(3,),(3,3,3),(3,1,3,1,3,1,3)),
strides = ((4,),(2,),(1,1,1),(2,1,1,1,1,1,1)),
lr=1, decay=1, flag_deconv=True,flag_deconv_relu=False,num_channel_deconv=128,scale_deconv=2,special_layers = "",addstrs = ""):
assert len(num_channels) == len(kernel_sizes) == len(strides)
for layer in xrange(len(num_channels)):
assert len(num_channels[layer]) == len(kernel_sizes[layer]) == len(strides[layer])
num_sublayer = len(num_channels[layer])
for sub_layer in xrange(num_sublayer):
if num_channels[layer] == 1:
out_layer = "conv{}".format(layer + 1)
else:
out_layer = "conv{}_{}".format(layer + 1, sub_layer + 1)
if out_layer == special_layers:
flag_bninplace = False
else:
flag_bninplace = True
out_layer += addstrs
num_output = num_channels[layer][sub_layer]
kernel_size = kernel_sizes[layer][sub_layer]
stride = strides[layer][sub_layer]
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=use_bn, use_relu=True,num_output=num_output,
kernel_size=kernel_size, pad=(kernel_size-1)/2, stride=stride,
use_scale=True, leaky=leaky,lr_mult=lr,decay_mult=decay,flag_bninplace=flag_bninplace)
if flag_bninplace:
from_layer = out_layer
else:
from_layer = out_layer + "_bn"
if flag_deconv:
deconv_param = {
'num_output': num_channel_deconv,
'kernel_size': scale_deconv,
'pad': 0,
'stride': scale_deconv,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_deconv = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': deconv_param
}
bn_kwargs = {
'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)],
'eps': 0.001,
}
sb_kwargs = {
'bias_term': True,
'param': [dict(lr_mult=1, decay_mult=0), dict(lr_mult=1, decay_mult=0)],
'filler': dict(type='constant', value=1.0),
'bias_filler': dict(type='constant', value=0.2),
}
from_layer = "conv{}_{}".format(len(num_channels), len(num_channels[-1])) + addstrs
add_layer = from_layer + "_deconv"
net[add_layer] = L.Deconvolution(net[from_layer], **kwargs_deconv)
if flag_deconv_relu:
bn_name = add_layer + '_bn'
net[bn_name] = L.BatchNorm(net[add_layer], in_place=True, **bn_kwargs)
sb_name = add_layer + '_scale'
net[sb_name] = L.Scale(net[add_layer], in_place=True, **sb_kwargs)
relu_name = add_layer + '_relu'
net[relu_name] = L.ReLU(net[add_layer], in_place=True)
return net
def mPose_StageXDeconv_Train(net, from_layer="concat_stage1", out_layer="concat_stage2", stage=1, \
mask_vec="vec_mask", mask_heat="heat_mask",label_vec="vec_label", label_heat="heat_label", \
use_3_layers=5, use_1_layers=2, short_cut=True,base_layer="convf", lr=1.0, decay=1.0,
num_channels = 128, kernel_size=3,flag_hasloss_befdecov=True,flag_reducefirst = False,up_scale=4,addstrs = ''):
kwargs = {'param': [dict(lr_mult=lr, decay_mult=decay), dict(lr_mult=2*lr, decay_mult=0)],
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0)}
assert from_layer in net.keys()
from1_layer = from_layer
from2_layer = from_layer
if use_1_layers > 0:
numlayers = use_3_layers + 1
else:
numlayers = use_3_layers
cnt = -1
if flag_reducefirst:
conv_vec = "stage{}_conv{}_vec".format(stage, 0) + addstrs
net[conv_vec] = L.Convolution(net[from1_layer], num_output=num_channels, pad=0,kernel_size=1, **kwargs)
relu_vec = "stage{}_relu{}_vec".format(stage, 0)
net[relu_vec] = L.ReLU(net[conv_vec], in_place=True)
from1_layer = relu_vec
# heat
conv_heat = "stage{}_conv{}_heat".format(stage, 0) + addstrs
net[conv_heat] = L.Convolution(net[from2_layer], num_output=num_channels, pad=0,kernel_size=1, **kwargs)
relu_heat = "stage{}_relu{}_heat".format(stage, 0)
net[relu_heat] = L.ReLU(net[conv_heat], in_place=True)
from2_layer = relu_heat
for layer in range(1, numlayers):
# vec
conv_vec = "stage{}_conv{}_vec".format(stage,layer) + addstrs
net[conv_vec] = L.Convolution(net[from1_layer], num_output=num_channels, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs)
relu_vec = "stage{}_relu{}_vec".format(stage,layer)
net[relu_vec] = L.ReLU(net[conv_vec], in_place=True)
from1_layer = relu_vec
# heat
conv_heat = "stage{}_conv{}_heat".format(stage,layer) + addstrs
net[conv_heat] = L.Convolution(net[from2_layer], num_output=num_channels, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs)
relu_heat = "stage{}_relu{}_heat".format(stage,layer)
net[relu_heat] = L.ReLU(net[conv_heat], in_place=True)
from2_layer = relu_heat
if use_1_layers > 0:
for layer in range(1, use_1_layers):
# vec
conv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers+layer) + addstrs
net[conv_vec] = L.Convolution(net[from1_layer], num_output=num_channels, pad=0, kernel_size=1, **kwargs)
relu_vec = "stage{}_relu{}_vec".format(stage,use_3_layers+layer) + addstrs
net[relu_vec] = L.ReLU(net[conv_vec], in_place=True)
from1_layer = relu_vec
# heat
conv_heat = "stage{}_conv{}_heat".format(stage,use_3_layers+layer) + addstrs
net[conv_heat] = L.Convolution(net[from2_layer], num_output=num_channels, pad=0, kernel_size=1, **kwargs)
relu_heat = "stage{}_relu{}_heat".format(stage,use_3_layers+layer) + addstrs
net[relu_heat] = L.ReLU(net[conv_heat], in_place=True)
from2_layer = relu_heat
# output
conv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers+use_1_layers) + addstrs
net[conv_vec] = L.Convolution(net[from1_layer], num_output=34, pad=0, kernel_size=1, **kwargs)
conv_heat = "stage{}_conv{}_heat".format(stage,use_3_layers+use_1_layers) + addstrs
net[conv_heat] = L.Convolution(net[from2_layer], num_output=18, pad=0, kernel_size=1, **kwargs)
else:
# output by 3x3
conv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers) + addstrs
net[conv_vec] = L.Convolution(net[from1_layer], num_output=34, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs)
conv_heat = "stage{}_conv{}_heat".format(stage,use_3_layers) + addstrs
net[conv_heat] = L.Convolution(net[from2_layer], num_output=18, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs)
if flag_hasloss_befdecov:
mask_vec_dn = mask_vec + "_DN"
label_vec_dn = label_vec + "_DN"
mask_heat_dn = mask_heat + "_DN"
label_heat_dn = label_heat + "_DN"
weight_vec = "weight_stage{}_vec".format(stage)
weight_heat = "weight_stage{}_heat".format(stage)
loss_vec = "loss_stage{}_vec".format(stage)
loss_heat = "loss_stage{}_heat".format(stage)
net[weight_vec] = L.Eltwise(net[conv_vec], net[mask_vec_dn], eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_vec] = L.EuclideanLoss(net[weight_vec], net[label_vec_dn], loss_weight=1)
net[weight_heat] = L.Eltwise(net[conv_heat], net[mask_heat_dn], eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_heat] = L.EuclideanLoss(net[weight_heat], net[label_heat_dn], loss_weight=1)
conv_param_vec = {
'num_output': 34,
'kernel_size': up_scale,
'pad': 0,
'stride': up_scale,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_vec = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': conv_param_vec
}
conv_param_heat = {
'num_output': 18,
'kernel_size': up_scale,
'pad': 0,
'stride': up_scale,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_heat = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': conv_param_heat
}
deconv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers) + '_deconv' + addstrs
net[deconv_vec] = L.Deconvolution(net[conv_vec], **kwargs_vec)
deconv_heat = "stage{}_conv{}_heat".format(stage, use_3_layers) + '_deconv' + addstrs
net[deconv_heat] = L.Deconvolution(net[conv_heat], **kwargs_heat)
weight_vec = "weight_stage{}_vec".format(stage) + '_deconv'
weight_heat = "weight_stage{}_heat".format(stage) + '_deconv'
loss_vec = "loss_stage{}_vec".format(stage) + '_deconv'
loss_heat = "loss_stage{}_heat".format(stage) + '_deconv'
net[weight_vec] = L.Eltwise(net[deconv_vec], net[mask_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_vec] = L.EuclideanLoss(net[weight_vec], net[label_vec], loss_weight=1)
net[weight_heat] = L.Eltwise(net[deconv_heat], net[mask_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_heat] = L.EuclideanLoss(net[weight_heat], net[label_heat], loss_weight=1)
if short_cut:
fea_layers = []
fea_layers.append(net[conv_vec])
fea_layers.append(net[conv_heat])
assert base_layer in net.keys()
fea_layers.append(net[base_layer])
net[out_layer] = L.Concat(*fea_layers, axis=1)
return net
def mPose_StageXDeconv_A_Train(net, from_layer="concat_stage1", out_layer="concat_stage2", stage=1, \
mask_vec="vec_mask", mask_heat="heat_mask",label_vec="vec_label", label_heat="heat_label", \
use_3_layers=5, use_1_layers=2, short_cut=True,base_layer="convf", lr=1.0, decay=1.0,
num_channels = 128, kernel_size=3,flag_reducefirst = False,scale_updown=1,addstrs = ''):
kwargs = {'param': [dict(lr_mult=lr, decay_mult=decay), dict(lr_mult=2*lr, decay_mult=0)],
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0)}
assert from_layer in net.keys()
from1_layer = from_layer
from2_layer = from_layer
if use_1_layers > 0:
numlayers = use_3_layers + 1
else:
numlayers = use_3_layers
cnt = -1
if flag_reducefirst:
conv_vec = "stage{}_conv{}_vec".format(stage, 0) + addstrs
net[conv_vec] = L.Convolution(net[from1_layer], num_output=num_channels, pad=0,kernel_size=1, **kwargs)
relu_vec = "stage{}_relu{}_vec".format(stage, 0)
net[relu_vec] = L.ReLU(net[conv_vec], in_place=True)
from1_layer = relu_vec
# heat
conv_heat = "stage{}_conv{}_heat".format(stage, 0) + addstrs
net[conv_heat] = L.Convolution(net[from2_layer], num_output=num_channels, pad=0,kernel_size=1, **kwargs)
relu_heat = "stage{}_relu{}_heat".format(stage, 0)
net[relu_heat] = L.ReLU(net[conv_heat], in_place=True)
from2_layer = relu_heat
for layer in range(1, numlayers):
# vec
conv_vec = "stage{}_conv{}_vec".format(stage,layer) + addstrs
net[conv_vec] = L.Convolution(net[from1_layer], num_output=num_channels, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs)
relu_vec = "stage{}_relu{}_vec".format(stage,layer)
net[relu_vec] = L.ReLU(net[conv_vec], in_place=True)
from1_layer = relu_vec
# heat
conv_heat = "stage{}_conv{}_heat".format(stage,layer) + addstrs
net[conv_heat] = L.Convolution(net[from2_layer], num_output=num_channels, pad=(kernel_size-1)/2, kernel_size=kernel_size, **kwargs)
relu_heat = "stage{}_relu{}_heat".format(stage,layer)
net[relu_heat] = L.ReLU(net[conv_heat], in_place=True)
from2_layer = relu_heat
conv_param_vec = {
'num_output': 34,
'kernel_size': scale_updown,
'pad': 0,
'stride': scale_updown,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_vec = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': conv_param_vec
}
conv_param_heat = {
'num_output': 18,
'kernel_size': scale_updown,
'pad': 0,
'stride': scale_updown,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_heat = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': conv_param_heat
}
conv_vec = "stage{}_conv{}_vec".format(stage,use_3_layers) + '_deconv' + addstrs
net[conv_vec] = L.Deconvolution(net[from1_layer], **kwargs_vec)
conv_heat = "stage{}_conv{}_heat".format(stage,use_3_layers) + '_deconv' + addstrs
net[conv_heat] = L.Deconvolution(net[from2_layer], **kwargs_heat)
weight_vec = "weight_stage{}_vec".format(stage) + '_deconv'
weight_heat = "weight_stage{}_heat".format(stage) + '_deconv'
loss_vec = "loss_stage{}_vec".format(stage) + '_deconv'
loss_heat = "loss_stage{}_heat".format(stage) + '_deconv'
net[weight_vec] = L.Eltwise(net[conv_vec], net[mask_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_vec] = L.EuclideanLoss(net[weight_vec], net[label_vec], loss_weight=1)
net[weight_heat] = L.Eltwise(net[conv_heat], net[mask_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_heat] = L.EuclideanLoss(net[weight_heat], net[label_heat], loss_weight=1)
if short_cut:
fea_layers = []
fea_layers.append(net[conv_vec])
fea_layers.append(net[conv_heat])
add_layer = "stage{}_concatvecandheat".format(stage)
net[add_layer] = L.Concat(*fea_layers, axis=1)
from_layer = add_layer
add_layer = add_layer + "_pool"
net[add_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX,
kernel_size=scale_updown, stride=scale_updown, pad=0)
fea_layers = []
fea_layers.append(net[add_layer])
assert base_layer in net.keys()
fea_layers.append(net[base_layer])
net[out_layer] = L.Concat(*fea_layers, axis=1)
return net
def YoloNetPart_StrideRemove1x1(net, num_sublayers, num_channels ,from_layer="data", lr=1, decay=1,alpha=1,fix_layer=-1,fix_sublayer = -1):
leaky = False
num_channels_fourlayers = [32,64,128,256]
flag_lr_zero = True
for layer in xrange(len(num_channels_fourlayers)):
for sublayer in xrange(num_sublayers[layer]):
if num_sublayers[layer] == 1:
out_layer = "conv{}".format(layer + 1)
else:
out_layer = "conv{}_{}".format(layer + 1, sublayer + 1)
if sublayer == num_sublayers[layer] - 1 and layer != len(num_channels_fourlayers) - 1:
stride = 2
else:
stride = 1
if layer != 0:
scale = alpha
else:
scale = 1
if fix_layer != -1:
if layer < fix_layer - 1:
flag_lr_zero = True
else:
flag_lr_zero = False
else:
flag_lr_zero = False
if flag_lr_zero:
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=(num_channels_fourlayers[layer] / scale), kernel_size=3, pad=1,
stride=stride, use_scale=True, leaky=leaky, lr_mult=0, decay_mult=0)
else:
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=(num_channels_fourlayers[layer]/scale), kernel_size=3, pad=1, stride=stride,
use_scale=True, leaky=leaky, lr_mult=lr, decay_mult=decay)
from_layer = out_layer
out_layer = 'pool4'
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX,
kernel_size=2, stride=2, pad=0)
for sublayer in xrange(len(num_channels)):
if sublayer != len(num_channels) - 1:
scale = alpha
else:
scale = 1
from_layer = out_layer
out_layer = "conv5_{}".format(sublayer + 1)
if fix_layer != -1:
if sublayer < fix_sublayer:
flag_lr_zero = True
else:
flag_lr_zero = False
else:
flag_lr_zero = False
if flag_lr_zero:
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=(num_channels[sublayer] / scale), kernel_size=3, pad=1, stride=1,
use_scale=True,leaky=leaky, lr_mult=0, decay_mult=0)
else:
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=(num_channels[sublayer]/scale), kernel_size=3, pad=1, stride=1, use_scale=True, leaky=leaky, lr_mult=lr,
decay_mult=decay)
return net
def YoloNetPart_StrideRemove1x1AndPooling(net, from_layer="data", lr=1, decay=1):
leaky = False
out_layer = 'conv1'
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, \
num_output=32, kernel_size=3, pad=1, stride=2, use_scale=True, leaky=leaky,lr_mult=lr,decay_mult=decay)
from_layer = out_layer
out_layer = 'conv2'
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=64, kernel_size=3, pad=1, stride=2, use_scale=True, leaky=leaky, lr_mult=lr,
decay_mult=decay)
from_layer = out_layer
out_layer = 'conv3_1'
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=128, kernel_size=3, pad=1, stride=2, use_scale=True, leaky=leaky, lr_mult=lr,
decay_mult=decay)
from_layer = out_layer
out_layer = 'conv3_2'
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=128, kernel_size=3, pad=1, stride=1, use_scale=True, leaky=leaky, lr_mult=lr,
decay_mult=decay)
from_layer = out_layer
out_layer = 'conv4_1'
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=256, kernel_size=3, pad=1, stride=1, use_scale=True, leaky=leaky, lr_mult=lr,
decay_mult=decay)
from_layer = out_layer
out_layer = 'conv4_2'
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=256, kernel_size=3, pad=1, stride=1, use_scale=True, leaky=leaky, lr_mult=lr,
decay_mult=decay)
num_channels = [512,256,512,256,512]
for sublayer in xrange(len(num_channels)):
from_layer = out_layer
out_layer = "conv5_{}".format(sublayer + 1)
if sublayer == 0:
stride = 2
else:
stride = 1
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=num_channels[sublayer], kernel_size=3, pad=1, stride=stride, use_scale=True, leaky=leaky, lr_mult=lr,
decay_mult=decay)
return net
def mPoseNet_COCO_DarkNetMultiStage_Train(net, data_layer="data", label_layer="label", train=True, **pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
net = YoloNetPart(net, from_layer=data_layer, use_bn=True, use_layers=5, use_sub_layers=5, final_pool=False, lr=1, decay=1)
baselayer = "convf"
net = UnifiedMultiScaleLayers(net, layers=["conv4_3","conv5_5"], tags=["Ref","Up"],unifiedlayer=baselayer, upsampleMethod="Reorg")
# Stages
net['convf_drop'] = L.SpatialDropout(net[baselayer], in_place=True,dropout_param=dict(dropout_ratio=0.2))
baselayer = 'convf_drop'
use_stage = 6
use_3_layers = 7
use_1_layers = 0
n_channel = 128
lrdecay = 1.0
kernel_size = 7
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size)
# for Test
if not train:
print(net.keys())
conv_vec = "stage{}_conv{}_vec".format(use_stage,use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage,use_3_layers + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64*64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_DarkNetMultiStageB_Train(net, data_layer="data", label_layer="label", train=True, **pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
flag_hasdrop = False
leaky = False
net = YoloNetPart(net, from_layer=data_layer, use_bn=True, use_layers=5, use_sub_layers=5, final_pool=False, leaky=leaky,lr=1, decay=1)
add_layer = "conv5_5_upsample"
conv_param = {"kernel_size":2,"stride":2,"num_output":128,"group":1,"pad":0,
"weight_filler":dict(type="bilinear"),"bias_term":False}
net[add_layer] = L.Deconvolution(net["conv5_5"],convolution_param=conv_param,param=dict(lr_mult=0, decay_mult=0))
fea_layers = []
fea_layers.append(net["conv4_3"])
fea_layers.append(net[add_layer])
add_layer = "multiscale_concat"
net[add_layer] = L.Concat(*fea_layers, axis=1)
# Stages
lrdecay = 1.0
baselayer = "convf"
ConvBNUnitLayer(net, add_layer, baselayer, use_bn=True, use_relu=True,num_output=128, kernel_size=3, pad=1, stride=1,
use_scale=True, leaky=leaky,lr_mult = lrdecay, decay_mult = lrdecay)
if flag_hasdrop:
net['convf_drop'] = L.SpatialDropout(net[baselayer], in_place=True, dropout_param=dict(dropout_ratio=0.2))
use_stage = 6
use_3_layers = 7
use_1_layers = 0
n_channel = 128
kernel_size = 7
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
if stage == use_stage - 1:
flag_change_layer = True
else:
flag_change_layer = False
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size,
flag_change_layer=flag_change_layer)
# for Test
if not train:
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64 * 64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9]),
}
for stage in xrange(use_stage):
conv_vec = "stage{}_conv{}_vec".format(stage + 1, use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(stage + 1, use_3_layers + use_1_layers)
net["vec_out{}".format(stage + 1)] = L.Eltwise(net.vec_mask, net[conv_vec],
eltwise_param=dict(operation=P.Eltwise.PROD))
net["heat_out{}".format(stage + 1)] = L.Eltwise(net.heat_mask, net[conv_heat],
eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net["heat_out{}".format(stage + 1)])
feaLayers.append(net["vec_out{}".format(stage + 1)])
outlayer = "concateval_stage{}".format(stage + 1)
net[outlayer] = L.Concat(*feaLayers, axis=1)
net["resized_map{}".format(stage + 1)] = L.ImResize(net[outlayer], name="resize",
imresize_param=resize_kwargs)
net["joints{}".format(stage + 1)] = L.Nms(net["resized_map{}".format(stage + 1)], name="nms",
nms_param=nms_kwargs)
net["limbs{}".format(stage + 1)] = L.Connectlimb(net["resized_map{}".format(stage + 1)],
net["joints{}".format(stage + 1)],
connect_limb_param=connect_kwargs)
net["eval{}".format(stage + 1)] = L.PoseEval(net["limbs{}".format(stage + 1)], net.gt,
pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_DarkNetMultiStageChangeBase_Train(net, data_layer="data", label_layer="label", train=True, **pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
flag_concat_lowlevel = True
flag_output_sigmoid = False
num_sublayers = [1, 1, 2, 3] #number of sublayers in conv1 to conv4
num_channels = [512, 128, 512, 256, 512] #number of channels in conv5
alpha = 1
leaky = False
num_chan_deconv = 128
ChangeNameAndChannel = {"conv4_3":128,"conv5_1":512}
# ChangeNameAndChannel = {}
net = YoloNetPart(net, from_layer=data_layer, use_bn=True, use_layers=5, use_sub_layers=5, final_pool=False,
leaky=leaky, lr=1, decay=1,ChangeNameAndChannel=ChangeNameAndChannel)
# net = YoloNetPart_StrideRemove1x1(net, num_sublayers=num_sublayers,num_channels=num_channels,from_layer=data_layer,
# lr=1, decay=1,alpha=alpha,fix_layer=5,fix_sublayer=1)
conv_param = {
'num_output': num_chan_deconv,
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_term': False,
'group': 1,
}
# conv_param = {"kernel_size": 4, "stride": 2, "num_output": 128, "group": 128, "pad": 1,
# "weight_filler": dict(type="bilinear"), "bias_term": False}
kwargs = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': conv_param
}
from_layer = "conv5_{}".format(len(num_channels))
out_layer = from_layer + "_Upsample"
net[out_layer] = L.Deconvolution(net[from_layer], **kwargs)
if flag_concat_lowlevel:
baselayer = "convf"
feature_layers = []
if "conv4_3" in ChangeNameAndChannel.keys():
feature_layers.append(net["conv4_3_new"])
else:
feature_layers.append(net["conv4_3"])
feature_layers.append(net[out_layer])
net[baselayer] = L.Concat(*feature_layers, axis=1)
else:
baselayer = out_layer
# Stages
use_stage = 3
use_3_layers = 5
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size,flag_sigmoid=flag_output_sigmoid)
# for Test
if not train:
if flag_output_sigmoid:
conv_vec = "stage{}_conv{}_vec".format(use_stage,use_3_layers + use_1_layers) + "_sig"
conv_heat = "stage{}_conv{}_heat".format(use_stage,use_3_layers + use_1_layers) + "_sig"
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64*64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_DarkNetSmallFeatMap_Train(net, data_layer="data", label_layer="label", train=True,**pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
flag_use_deconv = True
flag_concat_lowlevel = False
flag_has_other_loss = False
strid_convs = [1, 1, 1, 0, 0]
kernel_size_first = 7
stride_first = 4
channel_divides = (1, 1, 1, 1, 1)
num_channel_conv5_5 = 512
net = YoloNetPartCompress(net, from_layer="data", use_bn=True, use_layers=5, use_sub_layers=5,leaky=False,
strid_conv=strid_convs, final_pool=False, lr=1, decay=1,kernel_size_first=kernel_size_first,
stride_first = stride_first,channel_divides = channel_divides,num_channel_conv5_5=num_channel_conv5_5)
# if flag_use_deconv:
# down_sample_layers = ["vec_label", "heat_label","vec_mask","heat_mask"]
# for layer_name in down_sample_layers:
# out_layer = layer_name + "_DN"
# net[out_layer] = L.Pooling(net[layer_name], pool=P.Pooling.MAX,
# kernel_size=4, stride=4, pad=0)
if flag_concat_lowlevel:
from_layer = "conv4_3"
out_layer = from_layer + "_DN"
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.AVE,
kernel_size=2, stride=2, pad=0)
baselayer = "convf"
feature_layers = []
feature_layers.append(net[out_layer])
feature_layers.append(net["conv5_5"])
net[baselayer] = L.Concat(*feature_layers, axis=1)
else:
baselayer = "conv5_5"
# Stages
flag_hasloss_befdecov = False
flag_reducefirst = True
use_stage = 3
use_3_layers = [5,5,5]
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
# net = mPose_StageXDeconv_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
# mask_vec="vec_mask", mask_heat="heat_mask", \
# label_vec="vec_label", label_heat="heat_label", \
# use_3_layers=use_3_layers[stage], use_1_layers=use_1_layers, short_cut=short_cut, \
# base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size,
# flag_hasloss_befdecov=flag_hasloss_befdecov,flag_reducefirst=flag_reducefirst)
net = mPose_StageXDeconv_A_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage + 1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers[stage], use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,
kernel_size=kernel_size,flag_reducefirst=flag_reducefirst)
if train:
if flag_has_other_loss:
kwargs = {'param': [dict(lr_mult=lrdecay, decay_mult=lrdecay), dict(lr_mult=2 * lrdecay, decay_mult=0)],
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0)}
if not flag_concat_lowlevel:
from_layer = "conv4_3"
out_layer = from_layer + "_DN"
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.AVE,
kernel_size=2, stride=2, pad=0)
add_layers = ["conv4",]
from_layers = ["conv4_3_DN",]
kernel_size = 23
for i in xrange(len(add_layers)):
conv_vec = "%s_vec"%add_layers[i]
net[conv_vec] = L.Convolution(net[from_layers[i]], num_output=34, pad=(kernel_size - 1) / 2, kernel_size=kernel_size,
**kwargs)
conv_heat = "%s_heat"%add_layers[i]
net[conv_heat] = L.Convolution(net[from_layers[i]], num_output=18, pad=(kernel_size - 1) / 2, kernel_size=kernel_size,
**kwargs)
weight_vec = "weight_%s_vec"%add_layers[i]
weight_heat = "weight_%s_heat"%add_layers[i]
loss_vec = "loss_%s_vec"%add_layers[i]
loss_heat = "loss_%s_heat"%add_layers[i]
net[weight_vec] = L.Eltwise(net[conv_vec], net.vec_mask, eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_vec] = L.EuclideanLoss(net[weight_vec], net.vec_label, loss_weight=0.3)
net[weight_heat] = L.Eltwise(net[conv_heat], net.heat_mask, eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_heat] = L.EuclideanLoss(net[weight_heat], net.heat_label, loss_weight=0.5)
# for Test
if not train:
if flag_use_deconv:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers[-1] + use_1_layers) + '_deconv'
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers[-1] + use_1_layers) + '_deconv'
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers[-1] + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers[-1] + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64*64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_DarkNetSmallFeatMap_A_Train(net, data_layer="data", label_layer="label", train=True,**pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
flag_use_deconv = True
flag_concat_lowlevel = False
flag_has_other_loss = False
strid_convs = [1, 1, 1, 0, 0]
kernel_size_first = 7
stride_first = 4
channel_divides = (1, 1, 1, 1, 1)
num_channel_conv5_5 = 128
net = YoloNetPartCompress(net, from_layer="data", use_bn=True, use_layers=5, use_sub_layers=5,leaky=False,
strid_conv=strid_convs, final_pool=False, lr=1, decay=1,kernel_size_first=kernel_size_first,
stride_first = stride_first,channel_divides = channel_divides,num_channel_conv5_5=num_channel_conv5_5)
deconv_param = {
'num_output': 128,
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_deconv = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': deconv_param
}
from_layer = "conv5_5"
add_layer = from_layer + "_deconv"
net[add_layer] = L.Deconvolution(net[from_layer], **kwargs_deconv)
if flag_concat_lowlevel:
from_layer = "conv4_3"
baselayer = "convf"
feature_layers = []
feature_layers.append(net[from_layer])
feature_layers.append(net[add_layer])
net[baselayer] = L.Concat(*feature_layers, axis=1)
else:
baselayer = add_layer
up_scale = 2
if flag_use_deconv:
down_sample_layers = ["vec_label", "heat_label","vec_mask","heat_mask"]
for layer_name in down_sample_layers:
out_layer = layer_name + "_DN"
net[out_layer] = L.Pooling(net[layer_name], pool=P.Pooling.MAX,
kernel_size=up_scale, stride=up_scale, pad=0)
# Stages
flag_hasloss_befdecov = True
flag_reducefirst = True
use_stage = 3
use_3_layers = [5,5,5]
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageXDeconv_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers[stage], use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size,
flag_hasloss_befdecov=flag_hasloss_befdecov,flag_reducefirst=flag_reducefirst,up_scale=up_scale)
if train:
if flag_has_other_loss:
kwargs = {'param': [dict(lr_mult=lrdecay, decay_mult=lrdecay), dict(lr_mult=2 * lrdecay, decay_mult=0)],
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0)}
if not flag_concat_lowlevel:
from_layer = "conv4_3"
out_layer = from_layer + "_DN"
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.AVE,
kernel_size=2, stride=2, pad=0)
add_layers = ["conv4",]
from_layers = ["conv4_3_DN",]
kernel_size = 23
for i in xrange(len(add_layers)):
conv_vec = "%s_vec"%add_layers[i]
net[conv_vec] = L.Convolution(net[from_layers[i]], num_output=34, pad=(kernel_size - 1) / 2, kernel_size=kernel_size,
**kwargs)
conv_heat = "%s_heat"%add_layers[i]
net[conv_heat] = L.Convolution(net[from_layers[i]], num_output=18, pad=(kernel_size - 1) / 2, kernel_size=kernel_size,
**kwargs)
weight_vec = "weight_%s_vec"%add_layers[i]
weight_heat = "weight_%s_heat"%add_layers[i]
loss_vec = "loss_%s_vec"%add_layers[i]
loss_heat = "loss_%s_heat"%add_layers[i]
net[weight_vec] = L.Eltwise(net[conv_vec], net.vec_mask, eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_vec] = L.EuclideanLoss(net[weight_vec], net.vec_label, loss_weight=0.3)
net[weight_heat] = L.Eltwise(net[conv_heat], net.heat_mask, eltwise_param=dict(operation=P.Eltwise.PROD))
net[loss_heat] = L.EuclideanLoss(net[weight_heat], net.heat_label, loss_weight=0.5)
# for Test
if not train:
if flag_use_deconv:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers[-1] + use_1_layers) + '_deconv'
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers[-1] + use_1_layers) + '_deconv'
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers[-1] + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers[-1] + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64*64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_DarkNetSmallFeatMap_B_Train(net, data_layer="data", label_layer="label", train=True,**pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
flag_use_label_DN = False
scale_updown = 2
flag_concat_lowlevel = True
flag_concat_conv3 = True # default false
flag_upscale_base = True
flag_base_reduce = False
flag_concat_conv4_last = False# default False
use_sub_layers = (1, 1, 3, 3, 5)
num_channels = (32, 64, 128, 128, 256)
kernel_sizes = (7, 3, 3, 3, 3)
strides = (4,2,2,1,1)
num_channel_final = 128
strides_last_flags = (True,True,True,True,False)# default All True
ChangeNameLayers = ["conv1",]
for i in xrange(3):
ChangeNameLayers.append("conv{}_{}".format(4,i+1))
for i in xrange(5):
ChangeNameLayers.append("conv{}_{}".format(5,i+1))
net = YoloNetPartBigStride(net, from_layer=data_layer, use_bn=True, use_sub_layers=use_sub_layers, leaky=False,
num_channels = num_channels,kernel_sizes = kernel_sizes,strides = strides,
num_channel_final=num_channel_final,strides_last_flags=strides_last_flags,ChangeNameLayers=ChangeNameLayers,
lr=1, decay=1)
if flag_use_label_DN:
down_sample_layers = ["vec_label", "heat_label","vec_mask","heat_mask"]
for layer_name in down_sample_layers:
out_layer = layer_name + "_DN"
net[out_layer] = L.Pooling(net[layer_name], pool=P.Pooling.MAX,
kernel_size=scale_updown, stride=scale_updown, pad=0)
if flag_concat_lowlevel:
baselayer = "convf"
feature_layers = []
feature_layers.append(net["conv4_3_new"])
feature_layers.append(net["conv5_5_new"])
net[baselayer] = L.Concat(*feature_layers, axis=1)
else:
baselayer = "conv5_5_new"
if flag_base_reduce:
from_layer = baselayer
out_layer = from_layer + "_reduce"
ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, num_output=128,
kernel_size=1, pad=0, stride=1,
use_scale=True, leaky=False, lr_mult=1, decay_mult=1)
baselayer = out_layer
if flag_upscale_base:
deconv_param = {
'num_output': 128,
'kernel_size': scale_updown,
'pad': 0,
'stride': scale_updown,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_deconv = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': deconv_param
}
bn_kwargs = {
'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)],
'eps': 0.001,
}
sb_kwargs = {
'bias_term': True,
'param': [dict(lr_mult=1, decay_mult=0), dict(lr_mult=1, decay_mult=0)],
'filler': dict(type='constant', value=1.0),
'bias_filler': dict(type='constant', value=0.0),
}
from_layer = baselayer
add_layer = from_layer + "_deconv"
net[add_layer] = L.Deconvolution(net[from_layer], **kwargs_deconv)
bn_name = add_layer + '_bn'
net[bn_name] = L.BatchNorm(net[add_layer], in_place=True, **bn_kwargs)
sb_name = add_layer + '_scale'
net[sb_name] = L.Scale(net[add_layer], in_place=True, **sb_kwargs)
relu_name = add_layer + '_relu'
net[relu_name] = L.ReLU(net[add_layer], in_place=True)
baselayer = add_layer
if flag_concat_conv3:
feature_layers = []
feature_layers.append(net["conv3_2"])
feature_layers.append(net[baselayer])
add_layer = "concat345"
net[add_layer] = L.Concat(*feature_layers, axis=1)
baselayer = add_layer
if flag_concat_conv4_last:
feature_layers = []
feature_layers.append(net["conv4_3"])
feature_layers.append(net[baselayer])
add_layer = "concat4and5"
net[add_layer] = L.Concat(*feature_layers, axis=1)
baselayer = add_layer
# Stages
flag_hasloss_befdecov = True
flag_reducefirst = True
use_stage = 3
use_3_layers = [7,7,7]
use_1_layers = 0
n_channel = 128
lrdecay = 1.0
kernel_size = 3
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
##################################################################################################################################
# net = mPose_StageXDeconv_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
# mask_vec="vec_mask", mask_heat="heat_mask", \
# label_vec="vec_label", label_heat="heat_label", \
# use_3_layers=use_3_layers[stage], use_1_layers=use_1_layers, short_cut=short_cut, \
# base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size,
# flag_hasloss_befdecov=flag_hasloss_befdecov,flag_reducefirst=flag_reducefirst)
###################################################################################################################################
# net = mPose_StageXDeconv_A_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage + 1, \
# mask_vec="vec_mask", mask_heat="heat_mask", \
# label_vec="vec_label", label_heat="heat_label", \
# use_3_layers=use_3_layers[stage], use_1_layers=use_1_layers, short_cut=short_cut, \
# base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,
# kernel_size=kernel_size,flag_reducefirst=flag_reducefirst,scale_updown=scale_updown)
################################################################################################################################
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage + 1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers[stage], use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,
kernel_size=kernel_size)
# for Test
# for Test
if not train:
if not flag_hasloss_befdecov:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers[-1] + use_1_layers) + '_deconv'
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers[-1] + use_1_layers) + '_deconv'
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers[-1] + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers[-1] + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64*64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_DarkNetMultiStageChangeBaseA_Train(net, data_layer="data", label_layer="label", train=True, **pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
flag_concat_lowlevel = True
flag_output_sigmoid = False
num_sublayers = [1, 1, 2, 3]
num_channels = [512, 256, 512, 256, 512]
alpha = 1
net = YoloNetPart_StrideRemove1x1(net, num_sublayers=num_sublayers,num_channels=num_channels,from_layer=data_layer,
lr=1, decay=1,alpha=alpha)
conv_param = {
'num_output': 256,
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_term': False,
'group': 1,
}
# conv_param = {"kernel_size": 4, "stride": 2, "num_output": 128, "group": 128, "pad": 1,
# "weight_filler": dict(type="bilinear"), "bias_term": False}
kwargs = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': conv_param
}
from_layer = "conv5_{}".format(len(num_channels))
out_layer = from_layer + "_Upsample"
net[out_layer] = L.Deconvolution(net[from_layer], **kwargs)
if flag_concat_lowlevel:
add_layer = "multi_scale_concat"
feature_layers = []
feature_layers.append(net["conv4_3"])
feature_layers.append(net[out_layer])
net[add_layer] = L.Concat(*feature_layers, axis=1)
else:
add_layer = out_layer
baselayer = "convf"
ConvBNUnitLayer(net, add_layer, baselayer, use_bn=True, use_relu=True, num_output=128, kernel_size=3, pad=1,
stride=1,use_scale=True, leaky=False, lr_mult=1, decay_mult=1)
# Stages
use_stage = 3
use_3_layers = 6
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size,flag_sigmoid=flag_output_sigmoid)
# for Test
if not train:
if flag_output_sigmoid:
conv_vec = "stage{}_conv{}_vec".format(use_stage,use_3_layers + use_1_layers) + "_sig"
conv_heat = "stage{}_conv{}_heat".format(use_stage,use_3_layers + use_1_layers) + "_sig"
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64*64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_DarkNetMultiStageChangeBaseB_Train(net, data_layer="data", label_layer="label", train=True, **pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
# Darknet19
flag_concat_lowlevel = True
flag_output_sigmoid = False
num_sublayers = [1, 1, 2, 2]
num_channels = [512, 256, 512, 256, 512]
alpha = 1
net = YoloNetPart_StrideRemove1x1(net, num_sublayers=num_sublayers,num_channels=num_channels,from_layer=data_layer,
lr=1, decay=1,alpha=alpha)
conv_param = {
'num_output': 128,
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_term': False,
'group': 1,
}
# conv_param = {"kernel_size": 4, "stride": 2, "num_output": 128, "group": 128, "pad": 1,
# "weight_filler": dict(type="bilinear"), "bias_term": False}
kwargs = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': conv_param
}
from_layer = "conv5_{}".format(len(num_channels))
out_layer = from_layer + "_Upsample"
net[out_layer] = L.Deconvolution(net[from_layer], **kwargs)
if flag_concat_lowlevel:
add_layer = "multi_scale_concat"
feature_layers = []
feature_layers.append(net["conv4_2"])
feature_layers.append(net[out_layer])
net[add_layer] = L.Concat(*feature_layers, axis=1)
else:
add_layer = out_layer
baselayer = add_layer
# Stages
use_stage = 3
use_3_layers = 6
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage+1, \
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,kernel_size=kernel_size,flag_sigmoid=flag_output_sigmoid)
# for Test
if not train:
if flag_output_sigmoid:
conv_vec = "stage{}_conv{}_vec".format(use_stage,use_3_layers + use_1_layers) + "_sig"
conv_heat = "stage{}_conv{}_heat".format(use_stage,use_3_layers + use_1_layers) + "_sig"
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64*64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_SmallFeatMap_ReconBase_Train(net, data_layer="data",flag_withTea = True,loss_weight=1.0):
# Darknet19
use_sub_layers = (1, 1, 3, 3, 5)
num_channels = (32, 64, 128, 384, 512)
kernel_sizes = (7, 3, 3, 3, 3)
strides = (4,2,2,1,2)
strides_last_flags = (True,True,True,True,False)# default All True
pooling_flags = (False,) * 5
ChangeNameAndChannel = {}
deconv_channels = [128,128]
addstrs = "_recon"
net = YoloNetPartBigStride(net, from_layer=data_layer, use_bn=True, use_sub_layers=use_sub_layers, leaky=False,deconv_channels=deconv_channels,
num_channels = num_channels,kernel_sizes = kernel_sizes,strides = strides,strides_last_flags=strides_last_flags,
ChangeNameAndChannel=ChangeNameAndChannel,pooling_flags=pooling_flags,lr=1, decay=1,addstrs=addstrs)
if flag_withTea:
#### Teacher 15F
# strid_convs = [1, 1, 1, 0, 0]
# net = YoloNetPartCompress(net, from_layer="data", use_bn=True, use_layers=5, use_sub_layers=5,
# strid_conv=strid_convs, final_pool=False, lr=0, decay=0, leaky=True)
# add_layer = 'conv5_5_upsample'
# net[add_layer] = L.Reorg(net["conv5_5"], reorg_param=dict(up_down=P.Reorg.UP))
## Teach DarkTea8B
leaky = False
ChangeNameAndChannel = {"conv4_3": 128, "conv5_1": 512}
net = YoloNetPart(net, from_layer=data_layer, use_bn=True, use_layers=5, use_sub_layers=5, final_pool=False,
leaky=leaky, lr=0, decay=0, ChangeNameAndChannel=ChangeNameAndChannel)
####Both Teach DarkTea8B and DarkTea4A use the following deconv
conv_param = {
'num_output': 128,
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_term': False,
'group': 1,
}
# conv_param = {"kernel_size": 4, "stride": 2, "num_output": 128, "group": 128, "pad": 1,
# "weight_filler": dict(type="bilinear"), "bias_term": False}
kwargs = {
'param': [dict(lr_mult=0, decay_mult=0)],
'convolution_param': conv_param
}
from_layer = "conv5_5"
out_layer = from_layer + "_Upsample"
net[out_layer] = L.Deconvolution(net[from_layer], **kwargs)
ref_layer1 = "conv4_3_new"
ref_layer2 = "conv5_5_Upsample"
recon_layer1 = "conv4_{}_recon_deconv".format(use_sub_layers[3])
recon_layer2 = "conv5_{}_recon_deconv".format(use_sub_layers[4])
net['loss1'] = L.EuclideanLoss(net[ref_layer1], net[recon_layer1], loss_weight=loss_weight)
net['loss2'] = L.EuclideanLoss(net[ref_layer2], net[recon_layer2], loss_weight=loss_weight)
return net
def mPoseNet_COCO_SmallFeatMap_ReconBase_A_Train(net, data_layer="data", flag_withTea = True):
### For Student
num_channels = ((32,), (64,), (112, 56, 128, 64, 128), (176, 88, 176, 88, 208))
kernel_sizes = ((7,), (3,), (3, 1, 3, 1, 3), (3, 1, 3, 1, 3))
strides = ((4,), (2,), (1, 1, 1, 1, 1), (2, 1, 1, 1, 1))
num_channel_deconv = 128
scale_deconv = 2
addstrs = "_recon"
flag_deconv = True
flag_deconv_relu = False
special_layers = ""
net = Flexible_Base(net, from_layer="data", use_bn=True, leaky=False, num_channels=num_channels,
kernel_sizes=kernel_sizes, strides=strides, lr=1, decay=1, flag_deconv=flag_deconv,
flag_deconv_relu=flag_deconv_relu,num_channel_deconv=num_channel_deconv, scale_deconv=scale_deconv, special_layers=special_layers,
addstrs=addstrs)
recon_layer1 = "conv3_{}".format(len(num_channels[-2])) + addstrs
recon_layer2 = "conv4_{}".format(len(num_channels[-1])) + addstrs + "_deconv"
if flag_withTea:
###Teacher DarkNetTea8B
leaky = False
ChangeNameAndChannel = {"conv4_3":128,"conv5_1":512}
net = YoloNetPart(net, from_layer=data_layer, use_bn=True, use_layers=5, use_sub_layers=5, final_pool=False,
leaky=leaky, lr=0, decay=0, ChangeNameAndChannel=ChangeNameAndChannel)
### Teacher DarkNetTea4A
# num_sublayers_tea = [1, 1, 2, 3]
# num_channels_tea = [512, 256,512, 256,128]
# alpha = 1
# net = YoloNetPart_StrideRemove1x1(net, num_sublayers=num_sublayers_tea, num_channels=num_channels_tea,
# from_layer=data_layer,lr=0, decay=0,alpha=alpha,fix_layer=5,fix_sublayer=1)
conv_param = {
'num_output': 128,
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_term': False,
'group': 1,
}
# conv_param = {"kernel_size": 4, "stride": 2, "num_output": 128, "group": 128, "pad": 1,
# "weight_filler": dict(type="bilinear"), "bias_term": False}
kwargs = {
'param': [dict(lr_mult=0, decay_mult=0)],
'convolution_param': conv_param
}
from_layer = "conv5_5"
out_layer = from_layer + "_Upsample"
net[out_layer] = L.Deconvolution(net[from_layer], **kwargs)
ref_layer1 = "conv4_3_new"
ref_layer2 = "conv5_5_Upsample"
net['loss1'] = L.EuclideanLoss(net[ref_layer1], net[recon_layer1], loss_weight=1.0)
net['loss2'] = L.EuclideanLoss(net[ref_layer2], net[recon_layer2], loss_weight=1.0)
return net, recon_layer1, recon_layer2
def mPoseNet_COCO_SmallFeatMap_PoseFromRecon_Train(net, data_layer="data", label_layer="label", train=True,**pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
flag_deconv_explicit = False
# Darknet19
# use_sub_layers = (1, 1, 3, 3, 5)
# num_channels = (32, 64, 128, 256, 512)
# kernel_sizes = (7, 3, 3, 3, 3)
# strides = (4,2,2,1,2)
# strides_last_flags = (True,True,True,True,False)# default All True
# pooling_flags = (False,) * 5
# ChangeNameAndChannel = {"conv1":32}
# addstrs = ""
# net = YoloNetPartBigStride(net, from_layer=data_layer, use_bn=True, use_sub_layers=use_sub_layers, leaky=False,
# num_channels = num_channels,kernel_sizes = kernel_sizes,strides = strides,strides_last_flags=strides_last_flags,
# ChangeNameAndChannel=ChangeNameAndChannel,pooling_flags=pooling_flags,lr=1, decay=1,addstrs=addstrs)
num_channels = ((32,), (64,), (64, 32, 128), (256, 64, 256, 64, 256, 128, 128))
kernel_sizes = ((7,), (3,), (3, 3, 3), (3, 1, 3, 1, 3, 1, 3))
strides = ((4,), (2,), (1, 1, 1), (2, 1, 1, 1, 1, 1, 1))
num_channel_deconv = 128
scale_deconv = 2
addstrs = "_recon"
flag_deconv = True
flag_deconv_relu = False
special_layers = ""
net = Flexible_Base(net, from_layer=data_layer, use_bn=True, leaky=False, num_channels=num_channels,
kernel_sizes=kernel_sizes, strides=strides, lr=1, decay=1, flag_deconv=flag_deconv,
flag_deconv_relu=flag_deconv_relu,
num_channel_deconv=num_channel_deconv, scale_deconv=scale_deconv, special_layers=special_layers,
addstrs=addstrs)
if flag_deconv_explicit:
deconv_param = {
'num_output': 256,
'kernel_size': 2,
'pad': 0,
'stride': 2,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_deconv = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': deconv_param
}
bn_kwargs = {
'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)],
'eps': 0.001,
}
sb_kwargs = {
'bias_term': True,
'param': [dict(lr_mult=1, decay_mult=0), dict(lr_mult=1, decay_mult=0)],
'filler': dict(type='constant', value=1.0),
'bias_filler': dict(type='constant', value=0.2),
}
from_layer = "conv4_3" + addstrs
add_layer = from_layer + "_deconv"
print from_layer, add_layer
net[add_layer] = L.Deconvolution(net[from_layer], **kwargs_deconv)
bn_name = add_layer + '_bn'
net[bn_name] = L.BatchNorm(net[add_layer], in_place=True, **bn_kwargs)
sb_name = add_layer + '_scale'
net[sb_name] = L.Scale(net[add_layer], in_place=True, **sb_kwargs)
relu_name = add_layer + '_relu'
net[relu_name] = L.ReLU(net[add_layer], in_place=True)
deconv_param1 = {
'num_output': 128,
'kernel_size': 4,
'pad': 0,
'stride': 4,
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0),
'group': 1,
}
kwargs_deconv1 = {
'param': [dict(lr_mult=1, decay_mult=1)],
'convolution_param': deconv_param1
}
from_layer = "conv5_5" + addstrs
add_layer = from_layer + "_deconv"
net[add_layer] = L.Deconvolution(net[from_layer], **kwargs_deconv1)
bn_name = add_layer + '_bn'
net[bn_name] = L.BatchNorm(net[add_layer], in_place=True, **bn_kwargs)
sb_name = add_layer + '_scale'
net[sb_name] = L.Scale(net[add_layer], in_place=True, **sb_kwargs)
relu_name = add_layer + '_relu'
net[relu_name] = L.ReLU(net[add_layer], in_place=True)
feaLayers = []
feaLayers.append(net["conv3_3_recon"])
feaLayers.append(net["conv4_7_recon_deconv"])
baselayer = "convf"
net[baselayer] = L.Concat(*feaLayers, axis=1)
use_stage = 3
use_3_layers = 5
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
flag_output_sigmoid = False
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage + 1,
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,
kernel_size=kernel_size, flag_sigmoid=flag_output_sigmoid)
# for Test
if not train:
if flag_output_sigmoid:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers) + "_sig"
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers) + "_sig"
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64 * 64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_SmallFeatMap_PoseFromReconNew_Train(net, data_layer="data", label_layer="label", train=True,**pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net, ref_layer1, ref_layer2= mPoseNet_COCO_SmallFeatMap_ReconBase_A_Train(net, data_layer="data", flag_withTea=False)
feaLayers = []
feaLayers.append(net[ref_layer1])
feaLayers.append(net[ref_layer2])
baselayer = "convf"
net[baselayer] = L.Concat(*feaLayers, axis=1)
use_stage = 3
use_3_layers = 5
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
flag_output_sigmoid = False
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage + 1,
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,
kernel_size=kernel_size, flag_sigmoid=flag_output_sigmoid)
# for Test
if not train:
if flag_output_sigmoid:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers) + "_sig"
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers) + "_sig"
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64 * 64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
def mPoseNet_COCO_SmallFeatMap_PoseFromReconWithRecon_Train(net, data_layer="data", label_layer="label", train=True,**pose_test_kwargs):
# input
if train:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp = \
L.Slice(net[label_layer], ntop=4, slice_param=dict(slice_point=[34,52,86], axis=1))
else:
net.vec_mask, net.heat_mask, net.vec_temp, net.heat_temp, net.gt = \
L.Slice(net[label_layer], ntop=5, slice_param=dict(slice_point=[34,52,86,104], axis=1))
# label
net.vec_label = L.Eltwise(net.vec_mask, net.vec_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_label = L.Eltwise(net.heat_mask, net.heat_temp, eltwise_param=dict(operation=P.Eltwise.PROD))
net = mPoseNet_COCO_SmallFeatMap_ReconBase_Train(net,data_layer,flag_withTea=train,loss_weight=0.2)
feaLayers = []
feaLayers.append(net["conv4_3_recon_deconv"])
feaLayers.append(net["conv5_5_recon_deconv"])
baselayer = "convf"
net[baselayer] = L.Concat(*feaLayers, axis=1)
use_stage = 3
use_3_layers = 5
use_1_layers = 0
n_channel = 64
lrdecay = 1.0
kernel_size = 3
flag_output_sigmoid = False
for stage in xrange(use_stage):
if stage == 0:
from_layer = baselayer
else:
from_layer = "concat_stage{}".format(stage)
outlayer = "concat_stage{}".format(stage + 1)
if stage == use_stage - 1:
short_cut = False
else:
short_cut = True
net = mPose_StageX_Train(net, from_layer=from_layer, out_layer=outlayer, stage=stage + 1,
mask_vec="vec_mask", mask_heat="heat_mask", \
label_vec="vec_label", label_heat="heat_label", \
use_3_layers=use_3_layers, use_1_layers=use_1_layers, short_cut=short_cut, \
base_layer=baselayer, lr=lrdecay, decay=lrdecay, num_channels=n_channel,
kernel_size=kernel_size, flag_sigmoid=flag_output_sigmoid)
# for Test
if not train:
if flag_output_sigmoid:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers) + "_sig"
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers) + "_sig"
else:
conv_vec = "stage{}_conv{}_vec".format(use_stage, use_3_layers + use_1_layers)
conv_heat = "stage{}_conv{}_heat".format(use_stage, use_3_layers + use_1_layers)
net.vec_out = L.Eltwise(net.vec_mask, net[conv_vec], eltwise_param=dict(operation=P.Eltwise.PROD))
net.heat_out = L.Eltwise(net.heat_mask, net[conv_heat], eltwise_param=dict(operation=P.Eltwise.PROD))
feaLayers = []
feaLayers.append(net.heat_out)
feaLayers.append(net.vec_out)
outlayer = "concat_stage{}".format(3)
net[outlayer] = L.Concat(*feaLayers, axis=1)
# Resize
resize_kwargs = {
'factor': pose_test_kwargs.get("resize_factor", 8),
'scale_gap': pose_test_kwargs.get("resize_scale_gap", 0.3),
'start_scale': pose_test_kwargs.get("resize_start_scale", 1.0),
}
net.resized_map = L.ImResize(net[outlayer], name="resize", imresize_param=resize_kwargs)
# Nms
nms_kwargs = {
'threshold': pose_test_kwargs.get("nms_threshold", 0.05),
'max_peaks': pose_test_kwargs.get("nms_max_peaks", 100),
'num_parts': pose_test_kwargs.get("nms_num_parts", 18),
}
net.joints = L.Nms(net.resized_map, name="nms", nms_param=nms_kwargs)
# ConnectLimbs
connect_kwargs = {
'is_type_coco': pose_test_kwargs.get("conn_is_type_coco", True),
'max_person': pose_test_kwargs.get("conn_max_person", 10),
'max_peaks_use': pose_test_kwargs.get("conn_max_peaks_use", 20),
'iters_pa_cal': pose_test_kwargs.get("conn_iters_pa_cal", 10),
'connect_inter_threshold': pose_test_kwargs.get("conn_connect_inter_threshold", 0.05),
'connect_inter_min_nums': pose_test_kwargs.get("conn_connect_inter_min_nums", 8),
'connect_min_subset_cnt': pose_test_kwargs.get("conn_connect_min_subset_cnt", 3),
'connect_min_subset_score': pose_test_kwargs.get("conn_connect_min_subset_score", 0.4),
}
net.limbs = L.Connectlimb(net.resized_map, net.joints, connect_limb_param=connect_kwargs)
# Eval
eval_kwargs = {
'stride': 8,
'area_thre': pose_test_kwargs.get("eval_area_thre", 64 * 64),
'oks_thre': pose_test_kwargs.get("eval_oks_thre", [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9]),
}
net.eval = L.PoseEval(net.limbs, net.gt, pose_eval_param=eval_kwargs)
return net
| 49.693396
| 156
| 0.615263
| 14,346
| 105,350
| 4.182699
| 0.021888
| 0.024931
| 0.04363
| 0.049863
| 0.927606
| 0.915457
| 0.901592
| 0.888893
| 0.876844
| 0.872661
| 0
| 0.032606
| 0.253289
| 105,350
| 2,120
| 157
| 49.693396
| 0.730176
| 0.051742
| 0
| 0.798024
| 0
| 0
| 0.119066
| 0.023065
| 0
| 0
| 0
| 0
| 0.003842
| 0
| null | null | 0
| 0.003842
| null | null | 0.001647
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 1
| 1
| 1
| 1
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
|
0
| 8
|
309a8457c7c5be58f4ba804df9d03d706d0201d8
| 62
|
py
|
Python
|
bubbleZ/__init__.py
|
abatten/bubbleZ
|
20a895f4510dabd03426c307b40bd12c61edd39a
|
[
"MIT"
] | null | null | null |
bubbleZ/__init__.py
|
abatten/bubbleZ
|
20a895f4510dabd03426c307b40bd12c61edd39a
|
[
"MIT"
] | null | null | null |
bubbleZ/__init__.py
|
abatten/bubbleZ
|
20a895f4510dabd03426c307b40bd12c61edd39a
|
[
"MIT"
] | null | null | null |
from .pipeline import write_output
from .pipeline import utils
| 31
| 34
| 0.854839
| 9
| 62
| 5.777778
| 0.666667
| 0.461538
| 0.692308
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| 2
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0
| 7
|
309bce51c72c15bcd1858aa2aeb62ad0b60800fa
| 57,577
|
py
|
Python
|
venue/migrations/0002_auto_20180710_0208.py
|
Volentix/venue-server
|
9d6b3580516ab321f98c48ce1151671086145841
|
[
"MIT"
] | 7
|
2018-08-01T16:30:01.000Z
|
2018-12-10T05:12:27.000Z
|
venue/migrations/0002_auto_20180710_0208.py
|
Volentix/venue-server
|
9d6b3580516ab321f98c48ce1151671086145841
|
[
"MIT"
] | 103
|
2018-08-02T15:23:02.000Z
|
2018-12-13T03:48:15.000Z
|
venue/migrations/0002_auto_20180710_0208.py
|
Volentix/venue-server
|
9d6b3580516ab321f98c48ce1151671086145841
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.6 on 2018-06-19 04:05
from __future__ import unicode_literals
from django.db import migrations
from django.utils import timezone
from django.conf import settings
from django.contrib.auth.hashers import make_password
import uuid
LANGUAGES = [
{
'id': '390887e3-ff01-424e-a3a0-6f3c2fb9dfce',
'name': 'English',
'code': 'en'
},
{
'id': '7fe3056f-6924-4c4a-8a89-395f7f23419c',
'name': 'French',
'code': 'fr'
},
{
'id': '22ac9a32-d572-4c20-b174-bf642a5e82ce',
'name': 'Japanese',
'code': 'jp'
},
{
'id': 'a0236dbd-2749-46d6-a904-d26f8877cf18',
'name': 'Spanish',
'code': 'es'
},
{
'id': 'a5a08842-3c77-4810-ad0c-e6d3c14c3b6f',
'name': 'Portuguese',
'code': 'pt'
},
{
'id': '98f69eef-f1c3-4c90-8dff-f13271b0c957',
'name': 'Russian',
'code': 'ru'
},
{
'id': '0fb27814-ce14-4c48-9506-7bcbcf5acb1a',
'name': 'Chinese',
'code': 'zh'
},
{
'id': 'adc87e62-9662-43fb-9d1a-08878e715713',
'name': 'Korean',
'code': 'ko'
}
]
def add_languages(apps, schema_editor):
Language = apps.get_model('venue', 'Language')
for lang_details in LANGUAGES:
lang = Language(**lang_details)
lang.save()
# Admin superuser
USERS = [
{
'password': 'admin',
'is_superuser': True,
'username': 'admin',
'email': 'joemar.ct+admin@gmail.com',
'is_staff': True,
'is_active': True
}
]
# Other users
USERS += [
{
'password': 'default2018',
'username': 'stingray',
'email': 'joemar.ct+stingray@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'spiderman',
'email': 'joemar.ct+spiderman@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'thor',
'email': 'joemar.ct+thor@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'ironman',
'email': 'joemar.ct+ironman@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'antman',
'email': 'joemar.ct+antman@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'silverclaw',
'email': 'joemar.ct+silverclaw@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'hulk',
'email': 'joemar.ct+hulk@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'nomad',
'email': 'joemar.ct+nomad@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'hawkeye',
'email': 'joemar.ct+hawkeye@gmail.com',
'is_active': True
},
{
'password': 'default2018',
'username': 'wolverine',
'email': 'joemar.ct+wolverine@gmail.com',
'is_active': True
},
]
def add_users(apps, schema_editor):
User = apps.get_model('auth', 'User')
for user_details in USERS:
user = User(**user_details)
user.password = make_password(user_details['password'])
user.save()
def add_user_profiles(apps, schema_editor):
UserProfile = apps.get_model('venue', 'UserProfile')
User = apps.get_model('auth', 'User')
for user in USERS:
if 'is_superuser' not in user.keys():
user = User.objects.get(email=user['email'])
profile_details = {
'id': uuid.uuid4(),
'user': user,
'language_id': '390887e3-ff01-424e-a3a0-6f3c2fb9dfce',
'email_confirmed': True,
'receive_emails': True
}
user_profile = UserProfile(**profile_details)
user_profile.save()
FORUM_SITES = [
{
'id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'bitcointalk.org',
'address': 'https://bitcointalk.org',
'scraper_name': 'bitcointalk.py'
}
]
def add_forum_sites(apps, schema_editor):
ForumSite = apps.get_model('venue', 'ForumSite')
for site_details in FORUM_SITES:
site = ForumSite(**site_details)
site.save()
FORUM_USER_RANKS = [
{
'id': '517e74d0-fd50-4043-b397-e70dc49d10f0',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Brand new',
'bonus_percentage': 0,
'allowed': False
},
{
'id': 'bc8bb1b2-e056-47ab-bcf1-7967a27dd6b4',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Newbie',
'bonus_percentage': 0,
'allowed': False
},
{
'id': '80ab6f32-774a-4043-884c-203ac6b6e218',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Jr. Member',
'bonus_percentage': 0,
'allowed': False
},
{
'id': '09a7ad5e-1da4-4e7b-8799-93cf70ceb6f0',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Member',
'bonus_percentage': 0,
'allowed': False
},
{
'id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Full Member',
'bonus_percentage': 0,
'allowed': True
},
{
'id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Sr. Member',
'bonus_percentage': 2,
'allowed': True
},
{
'id': '065d2e38-295e-4b31-9da8-2946aaee5be1',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Hero Member',
'bonus_percentage': 5,
'allowed': True
},
{
'id': '14d22df0-3462-492b-81c5-b5812d3ef777',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'name': 'Legendary',
'bonus_percentage': 5,
'allowed': True
}
]
def add_forum_user_ranks(apps, schema_editor):
ForumUserRank = apps.get_model('venue', 'ForumUserRank')
for rank_details in FORUM_USER_RANKS:
rank = ForumUserRank(**rank_details)
rank.save()
SIGNATURES = [
{
'id': '764ab6b6-fb92-40ad-839c-4f26ee86eed8',
'name': 'Sr. Member Design 1',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'
],
'code': '[center][table][tr][td][url=https://volentix.io/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.io/][font=arial][size=4pt][color=transparent].[/color][/size]\r\n[size=16pt][color=black]VOLENTIX[/color][/size][/font][/url][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=8pt][color=#232c3b]█████ 3rd Party DAPP Platform █████[/color][/size]\r\n[size=16pt][url=https://volentix.io/][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/url][/size][/b][/font][/center][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=8pt][url=http://][color=#232c3b]ANN[/color][/url]\r\n[url=https://twitter.com/Volentix][color=#232c3b]TWITTER[/color][/url]\r\n[url=https://t.me/volentix][color=#232c3b]TELEGRAM[/color][/url][/size][/b][/font][/center][/td][td][/td]\r\n\r\n[td][size=4pt][color=#f25a4a]▄\r\n█\r\n█\r\n█\r\n█\r\n▀[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=16pt][color=black]▬ • VENUE • ▬[/color][/size]\r\n[size=8pt][color=#232c3b]Social Rewards Platform[/color][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][size=4pt][color=#f25a4a]▄\r\n█\r\n█\r\n█\r\n█\r\n▀[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=8pt][color=#232c3b]Decentralized Exchange[/color][/size]\r\n[size=16pt][color=black]▬ • VDEX • ▬[/color][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][tt][size=2px][color=#1f3150] ▄████████▄▄ \r\n ▄██████████████▄ \r\n ▄████▀▀ ▀▀████▄ ▄▄\r\n ███▀ ▀██████ \r\n ███ ▄████▄ ██████\r\n ███ █▀ ▀█ ███████ \r\n███ ▐▌ ▄▄▄▄▄ \r\n ▐▌ ▐▄▄▄▄▄▌ \r\n █▄ ▐ ▌ ███ \r\n███████ ▀█▐▀▀▀▀▀▌ ███ \r\n██████ ▀▀▀▀▀ ███ \r\n██████▄ ▄███▀ \r\n▀▀ ▀████▄▄ ▄▄████▀ \r\n ▀██████████████▀\r\n ▀▀███████▀▀[/color][/size][/tt][/td]\r\n\r\n[td][size=4pt][color=#f25a4a]▄\r\n█\r\n█\r\n█\r\n█\r\n▀[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][tt][size=2px]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][/tr][/table][/center]',
'test_signature': 'Love For All, Hatred For None',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/sr_1.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': '0a7dd7cd-8f3b-48ca-989c-d4ec550b136d',
'name': 'Sr. Member Design 2',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'
],
'code': '[center][table][tr][td][center][font=arial][b][size=16pt]VESPUCCI[/size]\r\n[size=8pt][color=#232c3b]Analytical Engine[/color][/size][/b][/font][/center][/td][td][/td]\r\n\r\n[td][tt][size=2px][color=#232c3b]\r\n\r\n [color=#777777]▄████▄▄ ▄██[/color]\r\n [color=#777777]▄█▀ ▀▀███▀[/color]\r\n [color=#777777]▄███▀[/color]\r\n [color=#777777]▄▄██████▄▄ ▄███▀▀[/color] ▄███▄\r\n [color=#777777]▄████████████▄ ▄██▀[/color] ▄█████████▄\r\n [color=#777777]██████████████████ ██▀[/color] ▄███████████████▄\r\n [color=#777777]████████████████████[/color] ▄███████████████████▄\r\n[color=#777777]██████████[/color]███████████████████████████████████\r\n[color=#777777]█████████[/color]████████████████████████████████████\r\n[color=#777777]███████[/color]██████████████████████████████████████\r\n█████████████████████████████████████████████\r\n█████████████████████████████████████████████\r\n█████████████████████████████████████████████[/color][/size][/tt][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=16pt][color=black]▬ • VENUE • ▬[/color][/size]\r\n[size=8pt][color=#232c3b]Social Rewards Platform[/color][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][url=https://volentix.com/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.com/][font=arial][b][size=16pt][color=black]VOLENTIX[/color][/size]\r\n[size=8pt][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/size][/b][/font][/url][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=8pt][url=http://][color=#232c3b]ANN[/color][/url]\r\n[url=https://twitter.com/Volentix][color=#232c3b]TWITTER[/color][/url]\r\n[url=https://t.me/volentix][color=#232c3b]TELEGRAM[/color][/url][/size][/b][/font][/center][/td][td][/td]\r\n\r\n[td][center][tt][size=2px]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=16pt][color=black]▬ • VDEX • ▬[/color][/size]\r\n[size=8pt][color=#232c3b]Decentralized Exchange[/color][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][tt][size=2px][color=#232c3b] ▄██▄\r\n ▄▐████▌▄▄▄ ▄██▄\r\n ▄▄ ▀ ▀██▀ ▐████▌\r\n▐██▌ ▀▄▄ █ ▀██▀▀▄\r\n▄▀▀ ▄ ████ ▄ ▄ ▄█▄\r\n█ ▀▄▄▀ ▀▀▄ ▀ █ ▀█▀\r\n ▄ ▄▄▀██ ▄███▄ ▄▀▄ ▌\r\n███ ▄▐█████▌ ▄██▄\r\n ▀ ▄ ▄ ▄▀ ▀███▀ ▐████▌\r\n ███ ▄▀ ▀▄ ▀██▀\r\n ▀▀▄███▄ ▄██▄▀\r\n ███████ ▐████▌\r\n ▐███████▌▄▀ ▀██▀\r\n ███████\r\n ▀███▀[/color][/size][/tt][/td]\r\n\r\n[td][center][font=arial][b][size=16pt]VCHAIN[/size]\r\n[size=8pt]▬▬▬▬▬▬▬▬[/size][/b][/font][/center][/td][/tr][/table][/center]',
'test_signature': 'Change the world by being yourself',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/sr_2.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': 'dc45d74f-26f8-4359-a9a5-92ee8540651f',
'name': 'Sr. Member Design 3',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'
],
'code': '[center][table][tr][td][table][tr][td][url=https://volentix.io/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.io/][font=arial][size=4pt][color=transparent].[/color][/size]\r\n[size=16pt][color=black]VOLENTIX[/color][/size][/font][/url][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=16pt][url=https://volentix.io/][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/url][/size]\r\n[size=8pt][color=#f25a4a]▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬[/color] [url=http://][color=black]ANN[/color][/url][/size][/b][/font][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://twitter.com/Volentix][tt][size=2px][color=#00aced] ▄█████▄▄ \r\n█▄ ████████████▀ \r\n████▄ █████████████▀ \r\n▀████████▄▄ ████████████\r\n▄▄████████████████████████\r\n █████████████████████████\r\n ▀███████████████████████ \r\n █████████████████████ \r\n ▀█████████████████▀\r\n ▄█████████████▀\r\n▄▄███████████████▀ \r\n ▀▀▀▀▀▀▀▀▀▀▀[/color][/size][/tt][/url][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://t.me/volentix][tt][size=2px][color=#2ca5e0] ▄▄████\r\n ▄▄▄████████▌\r\n ▄▄▄███████▀▄█████\r\n ▄▄█████████▀▀ ▄██████▌\r\n▄▄███████████▀ ▄█████████\r\n ▀▀▀█████▀ ▄██████████▌\r\n ██ █████████████\r\n █▄ █████████████▌\r\n ▐█▄███▀▀████████\r\n ███▀ ▀▀████▌\r\n ▀▀█[/color][/size][/tt][/url][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=8pt][color=#232c3b]Analytical Engine • VESPUCCI | VCHAIN • Blockchain Technology\r\n[color=#f25a4a]▬▬▬▬▬▬▬▬▬▬ 3rd Party DAPP Platform ▬▬▬▬▬▬▬▬▬▬[/color]\r\nSocial Rewards Platform • VENUE | VDEX • Decentralized Exchange[/color][/size][/b][/font][/url][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][tt][size=2px][url=https://bitcointalk.org/index.php?topic=2221211][/url]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][/tr][/table][/center]',
'test_signature': 'Every moment is a fresh beginning',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/sr_3.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': '5c739ddd-2046-40d1-a058-ce833b27b458',
'name': 'Sr. Member Design 4',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'
],
'code': '[center][table][tr][td][table][tr][td][url=https://volentix.io/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.io/][font=arial][size=4pt][color=transparent].[/color][/size]\r\n[size=16pt][color=black]VOLENTIX[/color][/size][/font][/url][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=16pt][url=https://volentix.io/][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/url][/size]\r\n[size=8pt][color=#f25a4a]▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬[/color] [url=http://][color=black]ANN[/color][/url][/size][/b][/font][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://twitter.com/Volentix][tt][size=2px][color=#232c3b] ▄█████▄▄ \r\n█▄ ████████████▀ \r\n████▄ █████████████▀ \r\n▀████████▄▄ ████████████\r\n▄▄████████████████████████\r\n █████████████████████████\r\n ▀███████████████████████ \r\n █████████████████████ \r\n ▀█████████████████▀\r\n ▄█████████████▀\r\n▄▄███████████████▀ \r\n ▀▀▀▀▀▀▀▀▀▀▀[/color][/size][/tt][/url][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://t.me/volentix][tt][size=2px][color=#232c3b] ▄▄████\r\n ▄▄▄████████▌\r\n ▄▄▄███████▀▄█████\r\n ▄▄█████████▀▀ ▄██████▌\r\n▄▄███████████▀ ▄█████████\r\n ▀▀▀█████▀ ▄██████████▌\r\n ██ █████████████\r\n █▄ █████████████▌\r\n ▐█▄███▀▀████████\r\n ███▀ ▀▀████▌\r\n ▀▀█[/color][/size][/tt][/url][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=8pt][color=#232c3b]Analytical Engine • VESPUCCI | VCHAIN • Blockchain Technology\r\n[color=#f25a4a]▬▬▬▬▬▬▬▬▬▬ 3rd Party DAPP Platform ▬▬▬▬▬▬▬▬▬▬[/color]\r\nSocial Rewards Platform • VENUE | VDEX • Decentralized Exchange[/color][/size][/b][/font][/url][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][tt][size=2px][url=https://bitcointalk.org/index.php?topic=2221211][/url]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][/tr][/table][/center]',
'test_signature': 'Never regret anything that made you smile',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/sr_4.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': 'e85eb8bb-2657-4081-b48f-f5da75030b3b',
'name': 'Hero/Legendary Member Design 1',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'065d2e38-295e-4b31-9da8-2946aaee5be1',
'14d22df0-3462-492b-81c5-b5812d3ef777'
],
'code': '[center][table][tr][td][url=https://volentix.io/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.io/][font=arial][size=4pt][color=transparent].[/color][/size]\r\n[size=16pt][color=black]VOLENTIX[/color][/size][/font][/url][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=8pt][glow=#232c3b,2,300][color=white][color=transparent]...............[/color]3rd Party DAPP Platform[color=transparent]...............[/color][/color][/glow][/size]\r\n[size=16pt][url=https://volentix.io/][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/url][/size][/b][/font][/center][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=8pt][url=http://][color=#232c3b]ANN[/color][/url]\r\n[url=https://twitter.com/Volentix][color=#232c3b]TWITTER[/color][/url]\r\n[url=https://t.me/volentix][color=#232c3b]TELEGRAM[/color][/url][/size][/b][/font][/center][/td][td][/td]\r\n\r\n[td][size=4pt][color=#f25a4a]▄\r\n█\r\n█\r\n█\r\n█\r\n▀[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=16pt][color=black]▬ • VENUE • ▬[/color][/size]\r\n[size=8pt][glow=#232c3b,2,300][color=white][color=transparent]......[/color]Social Rewards Platform[color=transparent]......[/color][/color][/glow][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][size=4pt][color=#f25a4a]▄\r\n█\r\n█\r\n█\r\n█\r\n▀[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=8pt][glow=#232c3b,2,300][color=white][color=transparent]...[/color]Decentralized Exchange[color=transparent]...[/color][/color][/glow][/size]\r\n[size=16pt][color=black]▬ • VDEX • ▬[/color][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][tt][size=2px][color=#1f3150] ▄████████▄▄ \r\n ▄██████████████▄ \r\n ▄████▀▀ ▀▀████▄ ▄▄\r\n ███▀ ▀██████ \r\n ███ ▄████▄ ██████\r\n ███ █▀ ▀█ ███████ \r\n███ ▐▌ ▄▄▄▄▄ \r\n ▐▌ ▐▄▄▄▄▄▌ \r\n █▄ ▐ ▌ ███ \r\n███████ ▀█▐▀▀▀▀▀▌ ███ \r\n██████ ▀▀▀▀▀ ███ \r\n██████▄ ▄███▀ \r\n▀▀ ▀████▄▄ ▄▄████▀ \r\n ▀██████████████▀ \r\n ▀▀███████▀▀[/color][/size][/tt][/td]\r\n\r\n[td][size=4pt][color=#f25a4a]▄\r\n█\r\n█\r\n█\r\n█\r\n▀[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][tt][size=2px]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][/tr][/table][/center]',
'test_signature': 'Die with memories, not dreams',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/hero_legendary_1.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': '9537b3d7-4d7f-41ba-8055-be8ed5f8ea28',
'name': 'Hero/Legendary Member Design 2',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'065d2e38-295e-4b31-9da8-2946aaee5be1',
'14d22df0-3462-492b-81c5-b5812d3ef777'
],
'code': '[center][table][tr][td][center][font=arial][b][size=16pt]VESPUCCI[/size]\r\n[size=8pt][glow=#232c3b,2,300][color=white][color=transparent]....[/color]Analytical Engine[color=transparent]....[/color][/color][/glow][/size][/b][/font][/center][/td][td][/td]\r\n\r\n[td][tt][size=2px][color=#232c3b]\r\n\r\n [color=#777777]▄████▄▄ ▄██[/color]\r\n [color=#777777]▄█▀ ▀▀███▀[/color]\r\n [color=#777777]▄███▀[/color]\r\n [color=#777777]▄▄██████▄▄ ▄███▀▀[/color] ▄███▄\r\n [color=#777777]▄████████████▄ ▄██▀[/color] ▄█████████▄\r\n [color=#777777]██████████████████ ██▀[/color] ▄███████████████▄\r\n [color=#777777]████████████████████[/color] ▄███████████████████▄\r\n[color=#777777]██████████[/color]███████████████████████████████████\r\n[color=#777777]█████████[/color]████████████████████████████████████\r\n[color=#777777]███████[/color]██████████████████████████████████████\r\n█████████████████████████████████████████████\r\n█████████████████████████████████████████████\r\n█████████████████████████████████████████████[/color][/size][/tt][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=16pt][color=black]▬ • VENUE • ▬[/color][/size]\r\n[size=8pt][glow=#232c3b,2,300][color=white][color=transparent]......[/color]Social Rewards Platform[color=transparent]......[/color][/color][/glow][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][url=https://volentix.com/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.com/][font=arial][b][size=16pt][color=black]VOLENTIX[/color][/size]\r\n[size=8pt][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/size][/b][/font][/url][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=8pt][url=http://][color=#232c3b]ANN[/color][/url]\r\n[url=https://twitter.com/Volentix][color=#232c3b]TWITTER[/color][/url]\r\n[url=https://t.me/volentix][color=#232c3b]TELEGRAM[/color][/url][/size][/b][/font][/center][/td][td][/td]\r\n\r\n[td][center][tt][size=2px]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██\r\n██[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=16pt][color=black]▬ • VDEX • ▬[/color][/size]\r\n[size=8pt][glow=#232c3b,2,300][color=white][color=transparent]...[/color]Decentralized Exchange[color=transparent]...[/color][/color][/glow][/size][/b][/font][/url][/center][/td][td][/td]\r\n\r\n[td][tt][size=2px][color=#232c3b] ▄██▄\r\n ▄▐████▌▄▄▄ ▄██▄\r\n ▄▄ ▀ ▀██▀ ▐████▌\r\n▐██▌ ▀▄▄ █ ▀██▀▀▄\r\n▄▀▀ ▄ ████ ▄ ▄ ▄█▄\r\n█ ▀▄▄▀ ▀▀▄ ▀ █ ▀█▀\r\n ▄ ▄▄▀██ ▄███▄ ▄▀▄ ▌\r\n███ ▄▐█████▌ ▄██▄\r\n ▀ ▄ ▄ ▄▀ ▀███▀ ▐████▌\r\n ███ ▄▀ ▀▄ ▀██▀\r\n ▀▀▄███▄ ▄██▄▀\r\n ███████ ▐████▌\r\n ▐███████▌▄▀ ▀██▀\r\n ███████\r\n ▀███▀[/color][/size][/tt][/td]\r\n\r\n[td][center][font=arial][b][size=16pt]VCHAIN[/size]\r\n[size=8pt]▬▬▬▬▬▬▬▬[/size][/b][/font][/center][/td][/tr][/table][/center]',
'test_signature': 'Aspire to inspire before we expire',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/hero_legendary_2.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': '5a506e4d-f5b6-408e-9e00-39f5b7521351',
'name': 'Hero/Legendary Member Design 3',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'065d2e38-295e-4b31-9da8-2946aaee5be1',
'14d22df0-3462-492b-81c5-b5812d3ef777'
],
'code': '[center][table][tr][td][table][tr][td][url=https://volentix.io/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.io/][font=arial][size=4pt][color=transparent].[/color][/size]\r\n[size=16pt][color=black]VOLENTIX[/color][/size][/font][/url][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=16pt][url=https://volentix.io/][glow=#232c3b,2,300][color=transparent]...[/color][color=white]Decentralized[/color] [color=#f25a4a]Change[/color][color=transparent]...[/color][/glow][/url][/size]\r\n[size=8pt][color=#f25a4a]▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬[/color] [url=http://][color=black]ANN[/color][/url][/size][/b][/font][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://twitter.com/Volentix][tt][size=2px][color=#00aced] ▄█████▄▄ \r\n█▄ ████████████▀ \r\n████▄ █████████████▀ \r\n▀████████▄▄ ████████████\r\n▄▄████████████████████████\r\n █████████████████████████\r\n ▀███████████████████████ \r\n █████████████████████ \r\n ▀█████████████████▀\r\n ▄█████████████▀\r\n▄▄███████████████▀ \r\n ▀▀▀▀▀▀▀▀▀▀▀[/color][/size][/tt][/url][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://t.me/volentix][tt][size=2px][color=#2ca5e0] ▄▄████\r\n ▄▄▄████████▌\r\n ▄▄▄███████▀▄█████\r\n ▄▄█████████▀▀ ▄██████▌\r\n▄▄███████████▀ ▄█████████\r\n ▀▀▀█████▀ ▄██████████▌\r\n ██ █████████████\r\n █▄ █████████████▌\r\n ▐█▄███▀▀████████\r\n ███▀ ▀▀████▌\r\n ▀▀█[/color][/size][/tt][/url][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][url=https://volentix.io/][font=arial][b][size=8pt][color=#232c3b]Analytical Engine • VESPUCCI | VCHAIN • Blockchain Technology\r\n[glow=#f25a4a,2,300][color=white][color=transparent]....[/color]▬▬▬▬▬▬▬▬▬ 3rd Party DAPP Platform ▬▬▬▬▬▬▬▬▬[color=transparent]....[/color][/color][/glow]\r\nSocial Rewards Platform • VENUE | VDEX • Decentralized Exchange[/color][/size][/b][/font][/url][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][tt][size=2px][url=https://bitcointalk.org/index.php?topic=2221211][/url]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][/tr][/table][/center]',
'test_signature': 'Everything you can imagine is real',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/hero_legendary_3.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': 'ac84e67b-077f-453c-a1f8-b63bac745bf3',
'name': 'Hero/Legendary Member Design 4',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'065d2e38-295e-4b31-9da8-2946aaee5be1',
'14d22df0-3462-492b-81c5-b5812d3ef777'
],
'code': '[center][table][tr][td][table][tr][td][url=https://volentix.io/][tt][size=2px][color=black]█▌ ▐█\r\n██▌ ▐██\r\n█▐█▌ ▐█▌█\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n█ ▐█▌ ▐█▌ █\r\n▀███████████████████████▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█▌ ▐█▌ ▄█▀\r\n ▀█▄ ▐█ █▌ ▄█▀\r\n ▀█▄▐█▌▄█▀\r\n ▀███▀[/color][/size][/tt][/url][/td]\r\n\r\n[td][url=https://volentix.io/][font=arial][size=4pt][color=transparent].[/color][/size]\r\n[size=16pt][color=black]VOLENTIX[/color][/size][/font][/url][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=16pt][url=https://volentix.io/][glow=#232c3b,2,300][color=transparent]...[/color][color=white]Decentralized[/color] [color=#f25a4a]Change[/color][color=transparent]...[/color][/glow][/url][/size]\r\n[size=8pt][color=#f25a4a]▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬[/color] [url=http://][color=black]ANN[/color][/url][/size][/b][/font][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://twitter.com/Volentix][tt][size=2px][color=#232c3b] ▄█████▄▄ \r\n█▄ ████████████▀ \r\n████▄ █████████████▀ \r\n▀████████▄▄ ████████████\r\n▄▄████████████████████████\r\n █████████████████████████\r\n ▀███████████████████████ \r\n █████████████████████ \r\n ▀█████████████████▀\r\n ▄█████████████▀\r\n▄▄███████████████▀ \r\n ▀▀▀▀▀▀▀▀▀▀▀[/color][/size][/tt][/url][/td][td][/td]\r\n\r\n[td][size=6px][color=transparent].[/color][/size]\r\n[url=https://t.me/volentix][tt][size=2px][color=#232c3b] ▄▄████\r\n ▄▄▄████████▌\r\n ▄▄▄███████▀▄█████\r\n ▄▄█████████▀▀ ▄██████▌\r\n▄▄███████████▀ ▄█████████\r\n ▀▀▀█████▀ ▄██████████▌\r\n ██ █████████████\r\n █▄ █████████████▌\r\n ▐█▄███▀▀████████\r\n ███▀ ▀▀████▌\r\n ▀▀█[/color][/size][/tt][/url][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][font=arial][b][size=8pt][color=#232c3b]Analytical Engine • VESPUCCI | VCHAIN • Blockchain Technology\r\n[glow=#f25a4a,2,300][color=white][color=transparent]....[/color]▬▬▬▬▬▬▬▬▬ 3rd Party DAPP Platform ▬▬▬▬▬▬▬▬▬[color=transparent]....[/color][/color][/glow]\r\nSocial Rewards Platform • VENUE | VDEX • Decentralized Exchange[/color][/size][/b][/font][/center][/td][td][/td][td][/td]\r\n\r\n[td][size=2pt][color=#ec6d5a]\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█\r\n█[/color][/size][/td][td][/td][td][/td]\r\n\r\n[td][center][tt][size=2px]\r\n\r\n\r\n▄▄███████████▄▄\r\n▄█▀▀ ▀▀█▄\r\n▄█ █ █ █▄\r\n█▌ ██ ██ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ █ █ █ █ ▐█\r\n█▌ ▀█▀▀█▀▀█▀ ▐█\r\n▀█ ▀███▀ █▀\r\n▀█▄▄ ▄▄█▀\r\n▀▀███████████▀▀[/size][/tt][/center][/td]\r\n\r\n[td][size=5px][color=transparent].[/color][/size]\r\n[font=arial][b][size=13px][color=#e22b2b]BETA TESTER[/color][/size]\r\n[size=8px]Designed by Zpectrum[/size][/b][/font][/td][/tr][/table][/center]',
'test_signature': 'Simplicity is the ultimate sophistication',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/hero_legendary_4.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': 'b0d7c3bb-53bf-4ce7-afcb-b1efea7aa472',
'name': 'Full Member Design 1',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'99c3f7d6-8adf-4594-a7d3-b64d47488c5a'
],
'code': '[center][font=arial][b][size=8pt][url=https://volentix.io/][color=black][u] V O L E N T I X[/u][/color][/url] [color=#f25a4a]█[/color] [url=https://volentix.io/][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/url] [color=#f25a4a]█[/color] [url=https://volentix.io/][color=#232c3b]3rd Party DAPP Platform[/color][/url]\r\n[url=https://volentix.io/][color=black]▬ • VENUE • ▬[/color] [color=#232c3b]Social Rewards Platform[/color][/url] [color=#f25a4a]█[/color] [url=https://t.me/volentix][color=#232c3b]ANN[/color][/url] [url=https://twitter.com/Volentix][color=#232c3b]TWITTER[/color][/url] [url=https://t.me/volentix][color=#232c3b]TELEGRAM[/color][/url] [color=#f25a4a]█[/color] [url=https://volentix.io/][color=black]▬ • VDEX • ▬[/color] [color=#232c3b]Decentralized Exchange[/color][/url]\r\n▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ( V ) [color=#e22b2b]BETA TESTER[/color] | Designed by Zpectrum ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬[/size][/b][/font][/center]',
'test_signature': 'Whatever you do, do it well',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/full_1.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': 'ac84e67b-077f-453c-a1f8-b63bac745bf3',
'name': 'Full Member Design 2',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'99c3f7d6-8adf-4594-a7d3-b64d47488c5a'
],
'code': '[center][font=arial][b][size=8pt][color=#ec6d5a]▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬[/color]\r\n[url=https://volentix.com/][color=black]VESPUCCI |[/color] [color=#232c3b]Analytical Engine[/color] [color=black]►[/color] [color=black]VENUE |[/color] [color=#232c3b]Social Rewards Platform[/color] [color=#232c3b]▌[/color] [color=black]VOLENTIX[/color] | [color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color] [color=#232c3b]▐[/color] [color=black]VDEX |[/color] [color=#232c3b]Decentralized Exchange[/color] [color=black]◄[/color] [color=black][u]VCHAIN [/u][/color][/url]\r\n[color=#ec6d5a]▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ [color=#232c3b]| [url=http://][color=#232c3b]ANN[/color][/url]• [url=https://twitter.com/Volentix][color=#232c3b]TWITTER[/color][/url] • [url=https://t.me/volentix][color=#232c3b]TELEGRAM[/color][/url] █ [color=#232c3b]( V ) [color=#e22b2b]BETA TESTER[/color] •[/color] [color=#232c3b]Designed by Zpectrum[/color] | [/color] ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬[/color][/size][/b][/font][/center]',
'test_signature': 'What we think, we become',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/full_2.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
},
{
'id': 'c3961d42-e4cb-41a1-862a-54ba2d0463de',
'name': 'Full Member Design 3',
'forum_site_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'user_ranks': [
'99c3f7d6-8adf-4594-a7d3-b64d47488c5a'
],
'code': '[center][font=arial][b][size=8pt][url=https://volentix.io/][color=black][u] V O L E N T I X[/u][/color][/url] [color=#232c3b]█[/color] [url=https://volentix.io/][color=#232c3b]Decentralized[/color] [color=#f25a4a]Change[/color][/url] [color=#232c3b]█[/color] ( V ) [color=#e22b2b]BETA TESTER[/color] ★ Designed by Zpectrum\r\n[color=#f25a4a]▌▌ [url=https://volentix.io/][color=black]3rd Party DAPP Platform[/color][/url] ▌▌ ▬▬▬▬ [url=http://][color=black]ANN[/color][/url] [url=https://twitter.com/Volentix][color=#00aced]Twitter[/color][/url] [url=https://t.me/volentix][color=#2ca5e0]Telegram[/color][/url] ▬▬▬▬[/color]\r\n[color=#f25a4a]▌▌[/color] [url=https://volentix.io/][color=black]Analytical Engine • VESPUCCI[/color] [color=#f25a4a]|[/color] [color=black]VCHAIN • Blockchain Technology[/color] [color=#f25a4a]|[/color] [color=black]Social Rewards Platform • VENUE[/color] [color=#f25a4a]|[/color] [color=black]VDEX • Decentralized Exchange[/color][/url] [color=#f25a4a]▌▌[/color][/size][/b][/font][/center]',
'test_signature': 'All limitations are self-imposed',
'image': 'https://s3.ca-central-1.amazonaws.com/venue-static/signatures/full_3.png',
'active': True,
'date_added': '2018-06-14 12:03:50+00:00'
}
]
def add_signatures(apps, schema_editor):
Signature = apps.get_model('venue', 'Signature')
for sig_details in SIGNATURES:
rank = Signature(**sig_details)
rank.save()
FORUM_PROFILES = [
{
'id': 'b772b2f2-2e9f-4139-859c-933abe8e6ba2',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad',
'forum_username': 'stingray',
'forum_user_id': '2299143',
'signature_id': '0a7dd7cd-8f3b-48ca-989c-d4ec550b136d',
'verification_code': '1N5IwKzZ',
'active': True,
'verified': True,
'date_verified': '2018-06-14 12:48:05+00:00',
'date_added': '2018-06-14 12:37:54+00:00',
'date_updated': '2018-06-16 07:24:09.271000+00:00',
'dummy': True
},
{
'id': '709bc479-a411-40df-bc9a-7564da0f521e',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a',
'forum_username': 'spiderman',
'forum_user_id': '8271817',
'signature_id': 'ac84e67b-077f-453c-a1f8-b63bac745bf3',
'verification_code': 'ewWsQjxl',
'active': True,
'verified': True,
'date_verified': '2018-06-14 14:27:16+00:00',
'date_added': '2018-06-14 14:26:43+00:00',
'date_updated': '2018-06-16 07:25:34.201000+00:00',
'dummy': True
},
{
'id': '480dd458-9325-44ca-a4a2-ee4c0ccfa173',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a',
'forum_username': 'antman',
'forum_user_id': '1319410',
'signature_id': 'ac84e67b-077f-453c-a1f8-b63bac745bf3',
'verification_code': '1dnS82lj',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:34:27+00:00',
'date_added': '2018-06-10 03:19:07+00:00',
'date_updated': '2018-06-16 07:22:32.686000+00:00',
'dummy': True
},
{
'id': '6b4d6821-ccba-41a9-8625-2a1da174b93d',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a',
'forum_username': 'hawkeye',
'forum_user_id': '1544937',
'signature_id': 'b0d7c3bb-53bf-4ce7-afcb-b1efea7aa472',
'verification_code': '1Q7h8pk7',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:33:13+00:00',
'date_added': '2018-06-10 03:19:07+00:00',
'date_updated': '2018-06-16 07:25:20.047000+00:00',
'dummy': True
},
{
'id': 'ce52c2f5-ae68-4a31-83a5-01f96de5d78f',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad',
'forum_username': 'hulk',
'forum_user_id': '1058231',
'signature_id': '0a7dd7cd-8f3b-48ca-989c-d4ec550b136d',
'verification_code': 'eDxcn2KX',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:32:37+00:00',
'date_added': '2018-06-10 03:19:07+00:00',
'date_updated': '2018-06-16 07:25:51.218000+00:00',
'dummy': True
},
{
'id': 'bd57efbc-9b53-4c85-9e05-5f593b96581d',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a',
'forum_username': 'ironman',
'forum_user_id': '2552354',
'signature_id': 'ac84e67b-077f-453c-a1f8-b63bac745bf3',
'verification_code': '1XXCzjmA',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:32:08+00:00',
'date_added': '2018-06-10 03:19:07+00:00',
'date_updated': '2018-06-16 03:53:43.877000+00:00',
'dummy': True
},
{
'id': '19bb3f6b-52b6-4651-947e-b99c2566049b',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': '14d22df0-3462-492b-81c5-b5812d3ef777',
'forum_username': 'thor',
'forum_user_id': '1216831',
'signature_id': 'c3961d42-e4cb-41a1-862a-54ba2d0463de',
'verification_code': 'VAyig4md',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:31:44+00:00',
'date_added': '2018-06-10 03:19:07+00:00',
'date_updated': '2018-06-16 07:42:46.570000+00:00',
'dummy': True
},
{
'id': '404a09b4-34ce-4a54-a52e-af815d289b31',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a',
'forum_username': 'silverclaw',
'forum_user_id': '2796746',
'signature_id': 'ac84e67b-077f-453c-a1f8-b63bac745bf3',
'verification_code': '1ZLFBW9q',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:30:35+00:00',
'date_added': '2018-06-10 03:19:07+00:00',
'date_updated': '2018-06-16 03:53:29.320000+00:00',
'dummy': True
},
{
'id': 'b759e131-192f-4656-b977-3f6010900528',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad',
'forum_username': 'nomad',
'forum_user_id': '1306970',
'signature_id': '764ab6b6-fb92-40ad-839c-4f26ee86eed8',
'verification_code': 'eLxu55pW',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:29:00+00:00',
'date_added': '2018-06-10 03:19:07+00:00',
'date_updated': '2018-06-16 03:53:18.016000+00:00',
'dummy': True
},
{
'id': '5bb143a4-22e1-4ba6-b14b-00235595f585',
'forum_id': '4b8b11d1-14bb-46a7-ac8b-397587235b28',
'forum_rank_id': '14d22df0-3462-492b-81c5-b5812d3ef777',
'forum_username': 'wolverine',
'forum_user_id': '1551606',
'signature_id': '5a506e4d-f5b6-408e-9e00-39f5b7521351',
'verification_code': '18pTZWDa',
'active': True,
'verified': True,
'date_verified': '2018-06-10 03:27:46+00:00',
'date_added': '2018-06-10 03:27:56+00:00',
'date_updated': '2018-06-16 03:53:08.829000+00:00',
'dummy': True
}
]
def add_forum_profiles(apps, schema_editor):
UserProfile = apps.get_model('venue', 'UserProfile')
ForumProfile = apps.get_model('venue', 'ForumProfile')
for fp_details in FORUM_PROFILES:
fp_details['user_profile'] = UserProfile.objects.get(
user__username=fp_details['forum_username']
)
fp_details['last_scrape'] = timezone.now()
forum_profile = ForumProfile(**fp_details)
forum_profile.save()
FORUM_POSTS = [
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-14 14:32:28+00:00', 'date_matured': '2018-06-14 14:32:30+00:00', 'influence_bonus_pct': '0.00', 'influence_bonus_pts': '0.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '40108455', 'timestamp': '2018-06-13 14:18:40+00:00', 'topic_id': '4083764', 'total_points': '100.00', 'unique_content_length': 298, 'valid_sig_minutes': 1440, 'forum_profile_id': 'b772b2f2-2e9f-4139-859c-933abe8e6ba2', 'forum_rank_id': 'bc8bb1b2-e056-47ab-bcf1-7967a27dd6b4'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-14 14:31:56+00:00', 'date_matured': '2018-06-14 14:31:58+00:00', 'influence_bonus_pct': '0.00', 'influence_bonus_pts': '0.00', 'invalid_sig_minutes': 40, 'matured': True, 'message_id': '40109083', 'timestamp': '2018-06-13 14:29:15+00:00', 'topic_id': '4469212', 'total_points': '100.00', 'unique_content_length': 145, 'valid_sig_minutes': 1400, 'forum_profile_id':'709bc479-a411-40df-bc9a-7564da0f521e', 'forum_rank_id': 'bc8bb1b2-e056-47ab-bcf1-7967a27dd6b4'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-14 14:37:20+00:00', 'date_matured': '2018-06-14 14:37:18+00:00', 'influence_bonus_pct': '0.00', 'influence_bonus_pts': '0.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '40109519', 'timestamp': '2018-06-13 14:36:44+00:00', 'topic_id': '4468464', 'total_points': '100.00', 'unique_content_length': 121, 'valid_sig_minutes': 1440, 'forum_profile_id': 'b772b2f2-2e9f-4139-859c-933abe8e6ba2', 'forum_rank_id': 'bc8bb1b2-e056-47ab-bcf1-7967a27dd6b4'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-13 04:25:08+00:00', 'date_matured': '2018-06-13 04:20:08+00:00', 'influence_bonus_pct': '5.00', 'influence_bonus_pts': '5.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '39923976', 'timestamp': '2018-06-12 04:20:08+00:00', 'topic_id': '3830642', 'total_points': '105.00', 'unique_content_length': 554, 'valid_sig_minutes': 1440, 'forum_profile_id': '5bb143a4-22e1-4ba6-b14b-00235595f585', 'forum_rank_id': '14d22df0-3462-492b-81c5-b5812d3ef777'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-12 06:59:21+00:00', 'date_matured': '2018-06-12 06:58:21+00:00', 'influence_bonus_pct': '2.00', 'influence_bonus_pts': '2.00', 'invalid_sig_minutes': 64, 'matured': True, 'message_id': '818272', 'timestamp': '2018-06-11 06:58:21+00:00', 'topic_id': '7281', 'total_points': '102.00', 'unique_content_length': 210, 'valid_sig_minutes': 1376, 'forum_profile_id': 'b772b2f2-2e9f-4139-859c-933abe8e6ba2', 'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-12 07:04:48+00:00', 'date_matured': '2018-06-12 07:04:43+00:00', 'influence_bonus_pct': '2.00', 'influence_bonus_pts': '2.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '213422', 'timestamp': '2018-06-11 07:04:09+00:00', 'topic_id': '234', 'total_points': '102.00', 'unique_content_length': 319, 'valid_sig_minutes': 1440, 'forum_profile_id': 'b772b2f2-2e9f-4139-859c-933abe8e6ba2', 'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-13 07:06:09+00:00', 'date_matured': '2018-06-13 07:06:05+00:00', 'influence_bonus_pct': '2.00', 'influence_bonus_pts': '2.00', 'invalid_sig_minutes': 60, 'matured': True, 'message_id': '563441', 'timestamp': '2018-06-12 07:05:57+00:00', 'topic_id': '443', 'total_points': '102.00', 'unique_content_length': 187, 'valid_sig_minutes': 1380, 'forum_profile_id': 'b772b2f2-2e9f-4139-859c-933abe8e6ba2', 'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-16 07:21:44+00:00', 'date_matured': '2018-06-16 07:21:38+00:00', 'influence_bonus_pct': '0.00', 'influence_bonus_pts': '0.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '1283829', 'timestamp': '2018-06-15 07:21:32+00:00', 'topic_id': '968', 'total_points': '100.00', 'unique_content_length': 58, 'valid_sig_minutes': 1440, 'forum_profile_id': '480dd458-9325-44ca-a4a2-ee4c0ccfa173', 'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-16 07:28:53+00:00', 'date_matured': '2018-06-16 07:28:51+00:00', 'influence_bonus_pct': '0.00', 'influence_bonus_pts': '0.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '7853723', 'timestamp': '2018-06-15 07:28:45+00:00', 'topic_id': '865', 'total_points': '100.00', 'unique_content_length': 321, 'valid_sig_minutes': 1440, 'forum_profile_id': '6b4d6821-ccba-41a9-8625-2a1da174b93d', 'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-14 07:35:26+00:00', 'date_matured': '2018-06-14 07:35:15+00:00', 'influence_bonus_pct': '2.00', 'influence_bonus_pts': '2.00', 'invalid_sig_minutes': 40, 'matured': True, 'message_id': '8537231', 'timestamp': '2018-06-13 07:35:11+00:00', 'topic_id': '362', 'total_points': '102.00', 'unique_content_length': 256, 'valid_sig_minutes': 1400, 'forum_profile_id': 'ce52c2f5-ae68-4a31-83a5-01f96de5d78f', 'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-16 07:37:24+00:00', 'date_matured': '2018-06-16 07:37:21+00:00', 'influence_bonus_pct': '0.00', 'influence_bonus_pts': '0.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '7562617', 'timestamp': '2018-06-15 07:37:15+00:00', 'topic_id': '124', 'total_points': '100.00', 'unique_content_length': 67, 'valid_sig_minutes': 1440, 'forum_profile_id': 'bd57efbc-9b53-4c85-9e05-5f593b96581d', 'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-13 07:39:19+00:00', 'date_matured': '2018-06-13 07:39:14+00:00', 'influence_bonus_pct': '2.00', 'influence_bonus_pts': '2.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '7843626', 'timestamp': '2018-06-12 07:39:09+00:00', 'topic_id': '432', 'total_points': '102.00', 'unique_content_length': 290, 'valid_sig_minutes': 1440, 'forum_profile_id': 'b759e131-192f-4656-b977-3f6010900528', 'forum_rank_id': 'dd1bbfa7-a3c3-4163-8c0d-b04eb84db9ad'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-15 07:41:06+00:00', 'date_matured': '2018-06-15 07:41:00+00:00', 'influence_bonus_pct': '0.00', 'influence_bonus_pts': '0.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '7262621', 'timestamp': '2018-06-14 07:40:53+00:00', 'topic_id': '746', 'total_points': '100.00', 'unique_content_length': 90, 'valid_sig_minutes': 1440, 'forum_profile_id': '404a09b4-34ce-4a54-a52e-af815d289b31', 'forum_rank_id': '99c3f7d6-8adf-4594-a7d3-b64d47488c5a'},
{'base_points': '100.00', 'credited': True, 'date_credited': '2018-06-15 07:42:27+00:00', 'date_matured': '2018-06-15 07:42:23+00:00', 'influence_bonus_pct': '5.00', 'influence_bonus_pts': '5.00', 'invalid_sig_minutes': 0, 'matured': True, 'message_id': '7362618', 'timestamp': '2018-06-14 07:42:17+00:00', 'topic_id': '737', 'total_points': '105.00', 'unique_content_length': 230, 'valid_sig_minutes': 1440, 'forum_profile_id': '19bb3f6b-52b6-4651-947e-b99c2566049b', 'forum_rank_id': '14d22df0-3462-492b-81c5-b5812d3ef777'}
]
def add_forum_posts(apps, schema_editor):
ForumPost = apps.get_model('venue', 'ForumPost')
ForumProfile = apps.get_model('venue', 'ForumProfile')
for post_details in FORUM_POSTS:
fp = ForumProfile.objects.get(id=post_details['forum_profile_id'])
post_details['user_profile'] = fp.user_profile
forum_post = ForumPost(**post_details)
forum_post.save()
class Migration(migrations.Migration):
dependencies = [
('venue', '0001_initial'),
]
operations = [
migrations.RunPython(add_users),
migrations.RunPython(add_languages),
migrations.RunPython(add_forum_sites),
migrations.RunPython(add_forum_user_ranks),
migrations.RunPython(add_signatures),
migrations.RunPython(add_user_profiles),
migrations.RunPython(add_forum_profiles),
migrations.RunPython(add_forum_posts)
]
| 93.317666
| 4,084
| 0.519009
| 8,890
| 57,577
| 3.930034
| 0.085039
| 0.043391
| 0.013138
| 0.014769
| 0.855315
| 0.831187
| 0.806285
| 0.745692
| 0.714952
| 0.686216
| 0
| 0.119986
| 0.175209
| 57,577
| 616
| 4,085
| 93.469156
| 0.496557
| 0.001667
| 0
| 0.324653
| 1
| 0.038194
| 0.799829
| 0.239317
| 0
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| 0
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| 0
| 1
| 0.013889
| false
| 0.022569
| 0.010417
| 0
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| null | 0
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| 1
| 1
| 1
| 1
| 1
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| 1
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0
| 10
|
30cb8d67fcedce205e4c7becb15575cb61af8813
| 256
|
py
|
Python
|
ProteinBackbone/main/pdb_utils/__init__.py
|
Division-Bell-CCME18/ProteinBackboneDesign
|
ba5755274a15868dbe71ce405831c8d042cfb3f0
|
[
"MIT"
] | 1
|
2022-03-21T11:49:21.000Z
|
2022-03-21T11:49:21.000Z
|
ProteinBackbone/main/pdb_utils/__init__.py
|
Division-Bell-CCME18/ProteinBackboneDesign
|
ba5755274a15868dbe71ce405831c8d042cfb3f0
|
[
"MIT"
] | null | null | null |
ProteinBackbone/main/pdb_utils/__init__.py
|
Division-Bell-CCME18/ProteinBackboneDesign
|
ba5755274a15868dbe71ce405831c8d042cfb3f0
|
[
"MIT"
] | null | null | null |
from .pdb_utils import pdb_to_data, process_pdb_dataset, filter_pdb_dataset, update_pdb_info, gen_perturb, extract_sketch_info
__all__ = ["pdb_to_data", "process_pdb_dataset", "filter_pdb_dataset", "update_pdb_info", "gen_perturb", "extract_sketch_info"]
| 64
| 127
| 0.828125
| 39
| 256
| 4.74359
| 0.410256
| 0.216216
| 0.097297
| 0.172973
| 0.886486
| 0.886486
| 0.886486
| 0.886486
| 0.886486
| 0.886486
| 0
| 0
| 0.070313
| 256
| 3
| 128
| 85.333333
| 0.777311
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| 1
| 1
| 1
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| 1
| 1
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| null | 0
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| 1
| 0
| 0
| 0
|
0
| 10
|
ebaef64ecc3a1ffcae83a544f2ecd8ba81a2019e
| 105
|
py
|
Python
|
txmilter/__init__.py
|
flaviogrossi/txmilter
|
7ac2905472e23b6b4bdab1b9f0659bd91cf4476b
|
[
"MIT"
] | 1
|
2018-12-18T04:14:27.000Z
|
2018-12-18T04:14:27.000Z
|
txmilter/__init__.py
|
flaviogrossi/txmilter
|
7ac2905472e23b6b4bdab1b9f0659bd91cf4476b
|
[
"MIT"
] | null | null | null |
txmilter/__init__.py
|
flaviogrossi/txmilter
|
7ac2905472e23b6b4bdab1b9f0659bd91cf4476b
|
[
"MIT"
] | null | null | null |
from protocol import MilterProtocol
from protocol import MilterFactory
from message import MilterMessage
| 26.25
| 35
| 0.885714
| 12
| 105
| 7.75
| 0.583333
| 0.258065
| 0.387097
| 0
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| 0.114286
| 105
| 3
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| 35
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| 1
| 0
| 1
| 0
|
0
| 7
|
ebd8bfdd209263b6be0eea4530704dc06727f0f6
| 162
|
py
|
Python
|
COH/admin.py
|
git-tree/DjangoProject
|
c33436340e691e86d90d14c91fcf0eb64ea2fded
|
[
"Apache-2.0"
] | 3
|
2020-11-26T09:19:33.000Z
|
2021-02-22T08:30:51.000Z
|
COH/admin.py
|
git-tree/DjangoProject
|
c33436340e691e86d90d14c91fcf0eb64ea2fded
|
[
"Apache-2.0"
] | null | null | null |
COH/admin.py
|
git-tree/DjangoProject
|
c33436340e691e86d90d14c91fcf0eb64ea2fded
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib import admin
from .models import Test,User,Overtime,Countdown
# Register your models here.
admin.site.register([Test,User,Overtime,Countdown])
| 40.5
| 51
| 0.820988
| 23
| 162
| 5.782609
| 0.608696
| 0.120301
| 0.240602
| 0.37594
| 0
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| 0
| 0.080247
| 162
| 4
| 51
| 40.5
| 0.892617
| 0.160494
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| 1
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|
0
| 7
|
692303b0538ff5c699b24a82ffcc5b0ae054f39c
| 2,951
|
py
|
Python
|
sla_app/migrations/0006_auto_20181204_1913.py
|
SLAteam/sla-mgmt
|
4ec9c300e923c9e1ca15821ba1ab6d9c0c12f63e
|
[
"BSD-3-Clause"
] | null | null | null |
sla_app/migrations/0006_auto_20181204_1913.py
|
SLAteam/sla-mgmt
|
4ec9c300e923c9e1ca15821ba1ab6d9c0c12f63e
|
[
"BSD-3-Clause"
] | 7
|
2018-12-03T22:59:16.000Z
|
2018-12-31T12:41:20.000Z
|
sla_app/migrations/0006_auto_20181204_1913.py
|
SLAteam/sla-mgmt
|
4ec9c300e923c9e1ca15821ba1ab6d9c0c12f63e
|
[
"BSD-3-Clause"
] | null | null | null |
# Generated by Django 2.1.3 on 2018-12-04 19:13
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('sla_app', '0005_auto_20181203_2153'),
]
operations = [
migrations.RemoveField(
model_name='keyperformanceindicator',
name='apn',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='mcc',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='mnc',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_count',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_network_cause',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_no_response',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_nok',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_ok',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_per_user',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_per_user_nok',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_per_user_ok',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_sucess',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='transactions_user_cause',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_count',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_network_causes',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_no_response',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_nok',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_ok',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_partially_ok',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_sucess',
),
migrations.RemoveField(
model_name='keyperformanceindicator',
name='users_user_causes',
),
]
| 30.112245
| 49
| 0.575059
| 206
| 2,951
| 7.970874
| 0.228155
| 0.268575
| 0.332521
| 0.383678
| 0.881242
| 0.881242
| 0.777101
| 0.388551
| 0.138855
| 0
| 0
| 0.015704
| 0.331074
| 2,951
| 97
| 50
| 30.42268
| 0.816109
| 0.015249
| 0
| 0.692308
| 1
| 0
| 0.290978
| 0.223485
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.010989
| 0
| 0.043956
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
15c51dd19d3ef58a20086757910cd31859c631d0
| 327
|
py
|
Python
|
test/test_all.py
|
hsdp/python-cf-api
|
13fc605e2ea3b5c09cc8a556c58e8c36ae290c8c
|
[
"Apache-2.0"
] | 20
|
2018-01-19T20:19:02.000Z
|
2020-06-09T08:45:40.000Z
|
test/test_all.py
|
hsdp/python-cf-api
|
13fc605e2ea3b5c09cc8a556c58e8c36ae290c8c
|
[
"Apache-2.0"
] | 4
|
2018-01-20T00:24:27.000Z
|
2020-03-16T01:26:27.000Z
|
test/test_all.py
|
hsdp/python-cf-api
|
13fc605e2ea3b5c09cc8a556c58e8c36ae290c8c
|
[
"Apache-2.0"
] | 3
|
2020-02-19T22:56:50.000Z
|
2021-05-12T19:38:33.000Z
|
from cf_api.deploy_blue_green import BlueGreen
from cf_api.deploy_manifest import Deploy
from cf_api.deploy_service import DeployService
from cf_api.deploy_space import Space
from cf_api import dropsonde_util
from cf_api import logs_util
from cf_api import exceptions
from cf_api import routes_util
from cf_api import ssh_util
| 32.7
| 47
| 0.877676
| 58
| 327
| 4.637931
| 0.310345
| 0.200743
| 0.301115
| 0.27881
| 0.211896
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.110092
| 327
| 9
| 48
| 36.333333
| 0.924399
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
c6127d0f269396c3bc25815fcd43f1cf4719a612
| 77
|
py
|
Python
|
mdssdk/parsers/zone/__init__.py
|
akshatha-s13/mdssdk
|
615a5528d0af1201e8fe8f305c62b258e5433990
|
[
"Apache-2.0"
] | 4
|
2020-12-13T20:02:43.000Z
|
2022-02-27T23:36:58.000Z
|
mdssdk/parsers/zone/__init__.py
|
akshatha-s13/mdssdk
|
615a5528d0af1201e8fe8f305c62b258e5433990
|
[
"Apache-2.0"
] | 13
|
2020-09-23T07:30:15.000Z
|
2022-03-30T01:12:25.000Z
|
mdssdk/parsers/zone/__init__.py
|
akshatha-s13/mdssdk
|
615a5528d0af1201e8fe8f305c62b258e5433990
|
[
"Apache-2.0"
] | 12
|
2020-05-11T09:33:21.000Z
|
2022-03-18T11:11:28.000Z
|
from .show_zone import ShowZone
from .show_zone_status import ShowZoneStatus
| 25.666667
| 44
| 0.87013
| 11
| 77
| 5.818182
| 0.636364
| 0.25
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.103896
| 77
| 2
| 45
| 38.5
| 0.927536
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
c6399d36807f2b01f0047fc55c72a26b75d69f5d
| 36
|
py
|
Python
|
nod.py
|
m-karakus/rq-docker-supervisor
|
89de6e20b72bbbe442eaafa05eaa22b046cef3f8
|
[
"MIT"
] | null | null | null |
nod.py
|
m-karakus/rq-docker-supervisor
|
89de6e20b72bbbe442eaafa05eaa22b046cef3f8
|
[
"MIT"
] | null | null | null |
nod.py
|
m-karakus/rq-docker-supervisor
|
89de6e20b72bbbe442eaafa05eaa22b046cef3f8
|
[
"MIT"
] | null | null | null |
import time
def main():
return
| 9
| 11
| 0.638889
| 5
| 36
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.277778
| 36
| 4
| 12
| 9
| 0.884615
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 8
|
d69f9a5b000869ba72dfb3f9bbaccb691490891c
| 8,646
|
py
|
Python
|
RecoMuon/MuonIdentification/python/Identification/cutBasedMuonId_MuonPOG_V0_cff.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 852
|
2015-01-11T21:03:51.000Z
|
2022-03-25T21:14:00.000Z
|
RecoMuon/MuonIdentification/python/Identification/cutBasedMuonId_MuonPOG_V0_cff.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 30,371
|
2015-01-02T00:14:40.000Z
|
2022-03-31T23:26:05.000Z
|
RecoMuon/MuonIdentification/python/Identification/cutBasedMuonId_MuonPOG_V0_cff.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 3,240
|
2015-01-02T05:53:18.000Z
|
2022-03-31T17:24:21.000Z
|
import FWCore.ParameterSet.Config as cms
from PhysicsTools.SelectorUtils.centralIDRegistry import central_id_registry
cutBasedMuonId_MuonPOG_V0_loose = cms.PSet(
idName = cms.string("cutBasedMuonId-MuonPOG-V0-loose"),
isPOGApproved = cms.untracked.bool(False),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MuonTypeByOrCut"),
types = cms.vstring("PFMuon"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonTypeByOrCut"),
types = cms.vstring("GlobalMuon", "TrackerMuon"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
)
)
cutBasedMuonId_MuonPOG_V0_medium = cms.PSet(
idName = cms.string("cutBasedMuonId-MuonPOG-V0-medium"),
isPOGApproved = cms.untracked.bool(False),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MuonTypeByOrCut"),
types = cms.vstring("PFMuon"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonTypeByOrCut"),
types = cms.vstring("GlobalMuon", "TrackerMuon"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonTrackCut"),
innerTrack = cms.PSet(
minValidFraction = cms.double(0.8), ),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonSegmentCompatibilityCut"),
goodGLB = cms.PSet(
maxGlbNormChi2 = cms.double(3.0),
maxChi2LocalPos = cms.double(12.0),
maxTrkKink = cms.double(20.0),
),
minCompatGlb = cms.double(0.303),
minCompatNonGlb = cms.double(0.451),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
)
)
cutBasedMuonId_MuonPOG_V0_tight = cms.PSet(
idName = cms.string("cutBasedMuonId-MuonPOG-V0-tight"),
isPOGApproved = cms.untracked.bool(False),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MuonTypeByOrCut"),
types = cms.vstring("PFMuon"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonTypeByOrCut"),
types = cms.vstring("GlobalMuon"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("GlobalMuonPromptTightCut"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonTrackCut"),
innerTrack = cms.PSet(
minTrackerLayersWithMeasurement = cms.int32(6),
minNumberOfValidPixelHits = cms.int32(1) ),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonMatchCut"),
minNumberOfMatchedStations = cms.int32(2),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonDxyCut"),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
trackType = cms.string("muonBestTrack"),
maxDxy = cms.double(0.2),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonDzCut"),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
trackType = cms.string("muonBestTrack"),
maxDz = cms.double(0.5),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False) ),
)
)
cutBasedMuonId_MuonPOG_V0_soft = cms.PSet(
idName = cms.string("cutBasedMuonId-MuonPOG-V0-soft"),
isPOGApproved = cms.untracked.bool(False),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("TMOneStationTightCut"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonTrackCut"),
innerTrack = cms.PSet(
minTrackerLayersWithMeasurement = cms.int32(6),
minPixelLayersWithMeasurement = cms.int32(1),
trackQuality = cms.string("highPurity") ),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonDxyCut"),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
trackType = cms.string("innerTrack"),
maxDxy = cms.double(0.3),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonDzCut"),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
trackType = cms.string("innerTrack"),
maxDz = cms.double(20.0),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False) ),
)
)
cutBasedMuonId_MuonPOG_V0_highpt = cms.PSet(
idName = cms.string("cutBasedMuonId-MuonPOG-V0-highpt"),
isPOGApproved = cms.untracked.bool(True),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MuonTypeByOrCut"),
types = cms.vstring("GlobalMuon"),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonTrackCut"),
innerTrack = cms.PSet(
minTrackerLayersWithMeasurement = cms.int32(6),
minNumberOfValidPixelHits = cms.int32(1) ),
globalTrack = cms.PSet(minNumberOfValidMuonHits = cms.int32(1) ),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonMatchCut"),
minNumberOfMatchedStations = cms.int32(2),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonMomQualityCut"),
maxRelPtErr = cms.double(0.3),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonDxyCut"),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
trackType = cms.string("muonBestTrack"),
maxDxy = cms.double(0.2),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("MuonDzCut"),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
trackType = cms.string("muonBestTrack"),
maxDz = cms.double(0.5),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False) ),
)
)
central_id_registry.register(cutBasedMuonId_MuonPOG_V0_loose.idName , 'd19c494fb8227d7af3c8e29053b1934a')
central_id_registry.register(cutBasedMuonId_MuonPOG_V0_medium.idName, '1f4bb781e8d98b4cb281de5c9b3fd193')
central_id_registry.register(cutBasedMuonId_MuonPOG_V0_tight.idName , '4c815640b5477c514210d7cdbde98fe0')
central_id_registry.register(cutBasedMuonId_MuonPOG_V0_soft.idName , '12da9c6aae4d389980da3f463aac2afb')
central_id_registry.register(cutBasedMuonId_MuonPOG_V0_highpt.idName, '3f6a009a63dc9eb6af3f2de17d53c9fd')
| 51.159763
| 105
| 0.585242
| 716
| 8,646
| 7.00838
| 0.129888
| 0.064169
| 0.095656
| 0.077919
| 0.834795
| 0.834795
| 0.834795
| 0.782583
| 0.737744
| 0.737744
| 0
| 0.028092
| 0.304187
| 8,646
| 168
| 106
| 51.464286
| 0.806017
| 0
| 0
| 0.7125
| 0
| 0
| 0.127935
| 0.077848
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.0125
| 0
| 0.0125
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d6c87d8c138924bdac55c2be30a12cb1a8434956
| 188,847
|
py
|
Python
|
trial/cloud_segmentation_with_utility_scripts_and_keras1.py
|
statsu1990/kaggle_understanding_clouds
|
756c8271855d232167a76bd25f8bb81e7505a422
|
[
"MIT"
] | null | null | null |
trial/cloud_segmentation_with_utility_scripts_and_keras1.py
|
statsu1990/kaggle_understanding_clouds
|
756c8271855d232167a76bd25f8bb81e7505a422
|
[
"MIT"
] | 6
|
2020-01-28T23:08:31.000Z
|
2022-02-10T00:24:01.000Z
|
trial/cloud_segmentation_with_utility_scripts_and_keras1.py
|
statsu1990/kaggle_understanding_clouds
|
756c8271855d232167a76bd25f8bb81e7505a422
|
[
"MIT"
] | null | null | null |
from script.cloud_images_segmentation_utillity_script import *
from keras.models import load_model
from tta_wrapper import tta_segmentation
import numpy as np
from model import base_unet, myunet, mylosses, mydeeplab
from script import mygenerator as mygen
import os
import datetime
import cv2
import matplotlib.pyplot as plt
def read_img(img_shape, directory, dataframe):
list_IDs = dataframe.index
imgs = np.empty((len(list_IDs), *img_shape), dtype='uint8')
imageName_to_imageIdx_dict = []
for i, ID in enumerate(list_IDs):
if i % 100 == 0:
print("\r read img {0}/{1}".format(i+1, len(list_IDs)), end="")
img_name = dataframe['image'].loc[ID]
img_path = directory + img_name
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if np.isnan(img).any():
print('{0}, {1}'.format(i, ID))
imgs[i,] = img
imageName_to_imageIdx_dict.append((img_name, i))
imageName_to_imageIdx_dict = dict(imageName_to_imageIdx_dict)
print("")
return imgs, imageName_to_imageIdx_dict
def calc_mask(img_shape, target_df, dataframe, mask_shape=(1400, 2100)):
list_IDs = dataframe.index
Y = np.empty((len(list_IDs), *img_shape[:2], 4), dtype='uint8')
imageName_to_maskIdx_dict = []
for i, ID in enumerate(list_IDs):
if i % 100 == 0:
print("\r calc mask {0}/{1}".format(i+1, len(list_IDs)), end="")
img_name = dataframe['image'].loc[ID]
image_df = target_df[target_df['image'] == img_name]
rles = image_df['EncodedPixels'].values
masks = build_masks(rles, input_shape=mask_shape, reshape=img_shape[:2])
if np.isnan(masks).any():
print('{0}, {1}'.format(i, ID))
Y[i, ] = masks
imageName_to_maskIdx_dict.append((img_name, i))
imageName_to_maskIdx_dict = dict(imageName_to_maskIdx_dict)
print("")
return Y, imageName_to_maskIdx_dict
def post_process_in_black(pred_masks, img):
not_in_black = np.ones_like(pred_masks) * np.any(img!=0, axis=-1)[...,np.newaxis]
post_masks = pred_masks * not_in_black
return not_in_black
def run19110601():
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110601
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
X_train = X_train
X_val = X_val
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 32 # 32
EPOCHS = 30 #12
LEARNING_RATE = 0.001 #3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'uNet_19110601_v1.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5)
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110602():
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110602
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
#X_train = X_train[:40]
#X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 32 # 32
EPOCHS = 30 #12
LEARNING_RATE = 0.001 #3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'uNet_19110602_v2.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5)
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110603():
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110603
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
X_train = X_train
X_val = X_val
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 30 #12
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110603_v1.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5)
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110701():
DEBUG = False
SHOW_IMG = False
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110701
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110701_v2.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
albu.Cutout(num_holes=12, max_h_size=32, max_w_size=32, p=0.5),
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110702():
DEBUG = False
SHOW_IMG = False
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110702
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110702_v2.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
albu.Cutout(num_holes=12, max_h_size=32, max_w_size=32, p=0.5),
])
MIXUP_ALPHA = 0.4
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed,
mixup_alpha=MIXUP_ALPHA)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110703():
DEBUG = False
SHOW_IMG = False
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110703
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 5 #3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110703_v3.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
#albu.Cutout(num_holes=12, max_h_size=32, max_w_size=32, p=0.5),
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110704():
DEBUG = False
SHOW_IMG = False
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110704
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 5 #3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110704_v4.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05, p=0.5),
#albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
# border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
#albu.Cutout(num_holes=12, max_h_size=32, max_w_size=32, p=0.5),
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110705():
DEBUG = False
SHOW_IMG = False
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110705
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 5 #3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110705_v5.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05, p=0.5),
#albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
# border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
#albu.Cutout(num_holes=12, max_h_size=32, max_w_size=32, p=0.5),
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110706():
DEBUG = False
SHOW_IMG = False
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110706
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 5 #3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110706_v6.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05, p=0.5),
#albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
# border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
#albu.Cutout(num_holes=12, max_h_size=32, max_w_size=32, p=0.5),
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls02_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110707():
DEBUG = False
SHOW_IMG = True
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110707
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 5 #3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110707_v7.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
#albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
albu.Cutout(num_holes=8, max_h_size=32, max_w_size=32, p=0.5),
])
preproc_before_aug = False
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
preproc_before_aug=preproc_before_aug,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls02_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
#pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
#pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run19110708():
DEBUG = False
SHOW_IMG = True
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110708
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=19110303)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
if DEBUG:
X_train = X_train[:40]
X_val = X_val[:40]
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 16 # 32
EPOCHS = 60 if not DEBUG else 1
LEARNING_RATE = 3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 5 #3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'deeplav_19110708_v8.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
#albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5),
#albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05, p=0.5),
albu.ShiftScaleRotate(rotate_limit=20, shift_limit=0.1, scale_limit=0.05,
border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=0.5),
albu.RandomBrightness(limit=0.2, p=0.99),
#albu.Cutout(num_holes=8, max_h_size=32, max_w_size=32, p=0.5),
])
preproc_before_aug = False
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
if DEBUG:
for i in range(10):
plt.imshow(augmentation(image=train_imgs[i])['image'])
plt.show()
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
preproc_before_aug=preproc_before_aug,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v3(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = mydeeplab.mydeeplab_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
if DEBUG:
test = test[:10]
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
#pred_masks_post = post_process_in_black(pred_masks_post, test_imgs[test_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
#pred_masks_post = post_process_in_black(pred_masks_post, valid_imgs[valid_imageName_to_imageIdx_dict[filename]])
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
if SHOW_IMG:
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
if SHOW_IMG:
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
def run2():
result_dir = os.path.join('result', datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
seed = 19110303
seed_everything(seed)
warnings.filterwarnings("ignore")
# Load data
train = pd.read_csv('../input/train.csv')
submission = pd.read_csv('../input/sample_submission.csv')
# Preprocecss data
train['image'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
submission['image'] = submission['Image_Label'].apply(lambda x: x.split('_')[0])
test = pd.DataFrame(submission['image'].unique(), columns=['image'])
# Create one column for each mask
train_df = pd.pivot_table(train, index=['image'], values=['EncodedPixels'], columns=['label'], aggfunc=np.min).reset_index()
train_df.columns = ['image', 'Fish_mask', 'Flower_mask', 'Gravel_mask', 'Sugar_mask']
# Train and validation split
X_train, X_val = train_test_split(train_df, test_size=0.2, random_state=seed)
X_train['set'] = 'train'
X_val['set'] = 'validation'
test['set'] = 'test'
X_train = X_train
X_val = X_val
# Model parameters
#BACKBONE = 'mobilenetv2' #'resnet18'
BATCH_SIZE = 32 # 32
EPOCHS = 100 #12
LEARNING_RATE = 0.001 #3e-4
HEIGHT = 384
WIDTH = 480
CHANNELS = 3
N_CLASSES = 4
ES_PATIENCE = 5
RLROP_PATIENCE = 3
DECAY_DROP = 0.5
model_path = os.path.join(result_dir, 'uNet_191106_v1.h5')
#
#preprocessing = sm.get_preprocessing(BACKBONE)
def preprocessing(_img):
_img = (_img - 127.5) / 127.5
return _img
#
augmentation = albu.Compose([albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.ShiftScaleRotate(rotate_limit=30, shift_limit=0.1, p=0.5)
])
# Pre-process data
train_base_path = '../input/train_images/'
test_base_path = '../input/test_images/'
train_images_dest_path = '../proc_input/train_images/'
validation_images_dest_path = '../proc_input/validation_images/'
test_images_dest_path = '../proc_input/test_images/'
PRE_PROCESS_DATA = False
if PRE_PROCESS_DATA:
# Making sure directories don't exist
if os.path.exists(train_images_dest_path):
shutil.rmtree(train_images_dest_path)
if os.path.exists(validation_images_dest_path):
shutil.rmtree(validation_images_dest_path)
if os.path.exists(test_images_dest_path):
shutil.rmtree(test_images_dest_path)
# Creating train, validation and test directories
os.makedirs(train_images_dest_path)
os.makedirs(validation_images_dest_path)
os.makedirs(test_images_dest_path)
def preprocess_data(df, HEIGHT=HEIGHT, WIDTH=WIDTH):
'''
This function needs to be defined here, because it will be called with no arguments,
and must have the default parameters from the beggining of the notebook (HEIGHT and WIDTH)
'''
df = df.reset_index()
for i in range(df.shape[0]):
item = df.iloc[i]
image_id = item['image']
item_set = item['set']
if item_set == 'train':
preprocess_image(image_id, train_base_path, train_images_dest_path, HEIGHT, WIDTH)
if item_set == 'validation':
preprocess_image(image_id, train_base_path, validation_images_dest_path, HEIGHT, WIDTH)
if item_set == 'test':
preprocess_image(image_id, test_base_path, test_images_dest_path, HEIGHT, WIDTH)
# Pre-procecss train set
pre_process_set(X_train, preprocess_data)
# Pre-procecss validation set
pre_process_set(X_val, preprocess_data)
# Pre-procecss test set
pre_process_set(test, preprocess_data)
# read image
train_imgs, train_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), train_images_dest_path, X_train)
valid_imgs, valid_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), validation_images_dest_path, X_val)
# calc mask
train_masks, train_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_train)
valid_masks, valid_imageName_to_maskIdx_dict = calc_mask((HEIGHT, WIDTH, CHANNELS), train, X_val)
# Data generator
train_generator = mygen.DataGenerator2(
images=train_imgs,
imageName_to_imageIdx_dict=train_imageName_to_imageIdx_dict,
masks=train_masks,
imageName_to_maskIdx_dict=train_imageName_to_maskIdx_dict,
dataframe=X_train,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
augmentation=augmentation,
seed=seed)
valid_generator = mygen.DataGenerator2(
images=valid_imgs,
imageName_to_imageIdx_dict=valid_imageName_to_imageIdx_dict,
masks=valid_masks,
imageName_to_maskIdx_dict=valid_imageName_to_maskIdx_dict,
dataframe=X_val,
batch_size=BATCH_SIZE,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed)
#model = base_unet.unet(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
#model = myunet.unet_v1(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
model = myunet.unet_v2(input_shape=(HEIGHT, WIDTH, CHANNELS), num_class=4)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
#es = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss', mode='min', patience=RLROP_PATIENCE, factor=DECAY_DROP, min_lr=1e-6, verbose=1)
csvlogger = CSVLogger(os.path.join(result_dir, 'learning_log.csv'))
metric_list = [dice_coef, sm.metrics.iou_score]
#callback_list = [checkpoint, es, rlrop, csvlogger]
callback_list = [checkpoint, rlrop, csvlogger]
optimizer = RAdam(learning_rate=LEARNING_RATE, warmup_proportion=0.1)
model.compile(optimizer=optimizer, loss=mylosses.bce_ls01_dice_loss, metrics=metric_list)
model.summary()
STEP_SIZE_TRAIN = len(X_train)//BATCH_SIZE
STEP_SIZE_VALID = len(X_val)//BATCH_SIZE
history = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callback_list,
epochs=EPOCHS,
verbose=1).history
#model.save(model_path)
# Load model trained longer
#model = load_model('../input/cloud-seg-resnet18-trainedlonger/resnet18_trained_longer.h5', custom_objects={'RAdam':RAdam, 'binary_crossentropy_plus_dice_loss':mylosses.bce_ls01_dice_loss, 'dice_coef':dice_coef, 'iou_score':sm.metrics.iou_score, 'f1-score':sm.metrics.f1_score})
# Threshold and mask size tunning
# - Here we could use some kind of parameter search, but to simplify I'm using default values
class_names = ['Fish ', 'Flower', 'Gravel', 'Sugar ']
best_tresholds = [.5, .5, .5, .35]
best_masks = [25000, 20000, 22500, 15000]
for index, name in enumerate(class_names):
print('%s treshold=%.2f mask size=%d' % (name, best_tresholds[index], best_masks[index]))
# Model evaluation
train_metrics = get_metrics(model, train, X_train, train_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Train')
print(train_metrics)
validation_metrics = get_metrics(model, train, X_val, validation_images_dest_path, best_tresholds, best_masks, seed=seed, preprocessing=preprocessing, set_name='Validation')
print(validation_metrics)
# Apply model to test set
model = tta_segmentation(model, h_flip=True, v_flip=True, h_shift=(-10, 10), v_shift=(-10, 10), merge='mean')
test_imgs, test_imageName_to_imageIdx_dict = read_img((HEIGHT, WIDTH, CHANNELS), test_images_dest_path, test)
test_df = []
for i in range(0, test.shape[0], 300):
batch_idx = list(range(i, min(test.shape[0], i + 300)))
batch_set = test[batch_idx[0]: batch_idx[-1]+1]
test_generator = mygen.DataGenerator2(
images=test_imgs,
imageName_to_imageIdx_dict=test_imageName_to_imageIdx_dict,
masks=None,
imageName_to_maskIdx_dict=None,
dataframe=batch_set,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='predict',
shuffle=False)
#test_generator = DataGenerator(
# directory=test_images_dest_path,
# dataframe=batch_set,
# target_df=submission,
# batch_size=1,
# target_size=(HEIGHT, WIDTH),
# n_channels=CHANNELS,
# n_classes=N_CLASSES,
# preprocessing=preprocessing,
# seed=seed,
# mode='predict',
# shuffle=False)
preds = model.predict_generator(test_generator)
for index, b in enumerate(batch_idx):
filename = test['image'].iloc[b]
image_df = submission[submission['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_post'] = pred_rles_post
###
test_df.append(image_df)
sub_df = pd.concat(test_df)
# Inspecting some of the validation set predictions
# ## Without post-processing
# Choose 3 samples at random
images_to_inspect = np.random.choice(X_val['image'].unique(), 3, replace=False)
inspect_set = train[train['image'].isin(images_to_inspect)].copy()
inspect_set_temp = []
inspect_generator = DataGenerator(
directory=validation_images_dest_path,
dataframe=inspect_set,
target_df=train,
batch_size=1,
target_size=(HEIGHT, WIDTH),
n_channels=CHANNELS,
n_classes=N_CLASSES,
preprocessing=preprocessing,
seed=seed,
mode='fit',
shuffle=False)
preds = model.predict_generator(inspect_generator)
for index, b in enumerate(range(len(preds))):
filename = inspect_set['image'].iloc[b]
image_df = inspect_set[inspect_set['image'] == filename].copy()
pred_masks = preds[index, ].round().astype(int)
pred_rles = build_rles(pred_masks, reshape=(350, 525))
image_df['EncodedPixels_pred'] = pred_rles
### Post procecssing
pred_masks_post = preds[index, ].astype('float32')
for class_index in range(N_CLASSES):
pred_mask = pred_masks_post[...,class_index]
pred_mask = post_process(pred_mask, threshold=best_tresholds[class_index], min_size=best_masks[class_index])
pred_masks_post[...,class_index] = pred_mask
pred_rles_post = build_rles(pred_masks_post, reshape=(350, 525))
image_df['EncodedPixels_pred_post'] = pred_rles_post
###
inspect_set_temp.append(image_df)
inspect_set = pd.concat(inspect_set_temp)
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred')
# With post-processing
inspect_predictions(inspect_set, images_to_inspect, validation_images_dest_path, pred_col='EncodedPixels_pred_post')
# Inspecting some of the test set predictions
#
# Without post-process
# Choose 5 samples at random
images_to_inspect_test = np.random.choice(sub_df['image'].unique(), 4, replace=False)
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path)
# ## With post-process
inspect_predictions(sub_df, images_to_inspect_test, test_images_dest_path, label_col='EncodedPixels_post')
# Regular submission
submission_df = sub_df[['Image_Label' ,'EncodedPixels']]
submission_df.to_csv(os.path.join(result_dir, 'submission.csv'), index=False)
print(submission_df.head())
# Submission with post processing
submission_df_post = sub_df[['Image_Label' ,'EncodedPixels_post']]
submission_df_post.columns = ['Image_Label' ,'EncodedPixels']
submission_df_post.to_csv(os.path.join(result_dir, 'submission_post.csv'), index=False)
print(submission_df_post.head())
| 44.591972
| 283
| 0.608064
| 22,073
| 188,847
| 4.882889
| 0.018439
| 0.028948
| 0.040527
| 0.027528
| 0.987744
| 0.98728
| 0.986992
| 0.985637
| 0.985637
| 0.985637
| 0
| 0.019936
| 0.2932
| 188,847
| 4,235
| 284
| 44.591972
| 0.787544
| 0.154098
| 0
| 0.969388
| 0
| 0
| 0.064414
| 0.017171
| 0
| 0
| 0
| 0
| 0
| 1
| 0.014739
| false
| 0
| 0.003779
| 0
| 0.024187
| 0.024943
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d6e776f4d4e1020495ab77f3fb6a10174e1ac4b1
| 109
|
py
|
Python
|
tests/assets/server_t1.py
|
baturayo/dploy-kickstart
|
2f58a780241032cfaacfa91f1d834db1c91c7abb
|
[
"MIT"
] | 6
|
2020-05-20T11:56:42.000Z
|
2020-11-03T16:24:36.000Z
|
tests/assets/server_t1.py
|
baturayo/dploy-kickstart
|
2f58a780241032cfaacfa91f1d834db1c91c7abb
|
[
"MIT"
] | 9
|
2020-06-02T15:03:42.000Z
|
2020-11-12T11:55:48.000Z
|
tests/assets/server_t1.py
|
baturayo/dploy-kickstart
|
2f58a780241032cfaacfa91f1d834db1c91c7abb
|
[
"MIT"
] | 3
|
2020-09-10T13:38:02.000Z
|
2020-10-01T16:36:48.000Z
|
#' @dploy endpoint predict
def f1(x):
return x["val"]
#' @dploy endpoint train
def f2(x):
return x
| 12.111111
| 26
| 0.614679
| 17
| 109
| 3.941176
| 0.588235
| 0.38806
| 0.238806
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.024096
| 0.238532
| 109
| 8
| 27
| 13.625
| 0.783133
| 0.440367
| 0
| 0
| 0
| 0
| 0.050847
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
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| 1
| 0
|
0
| 7
|
d6f5bab39c9d9b7c4a4c647f6a4d9e09faa25fb7
| 20,126
|
py
|
Python
|
fixture/contact.py
|
baturov-dev/python_testing
|
54d4b9dcc68885ce29819deb114185a464b03366
|
[
"Apache-2.0"
] | null | null | null |
fixture/contact.py
|
baturov-dev/python_testing
|
54d4b9dcc68885ce29819deb114185a464b03366
|
[
"Apache-2.0"
] | null | null | null |
fixture/contact.py
|
baturov-dev/python_testing
|
54d4b9dcc68885ce29819deb114185a464b03366
|
[
"Apache-2.0"
] | null | null | null |
from model.contact import Contact
import re
class ContactHelper:
def __init__(self, app):
self.app = app
def create(self, group):
wd = self.app.wd
# init contact creation
wd.find_element_by_link_text("add new").click()
# fill contact form
wd.find_element_by_name("firstname").click()
wd.find_element_by_name("firstname").clear()
wd.find_element_by_name("firstname").send_keys(group.firstname)
wd.find_element_by_name("middlename").click()
wd.find_element_by_name("middlename").clear()
wd.find_element_by_name("middlename").send_keys(group.middlename)
wd.find_element_by_name("lastname").click()
wd.find_element_by_name("lastname").clear()
wd.find_element_by_name("lastname").send_keys(group.lastname)
wd.find_element_by_name("nickname").click()
wd.find_element_by_name("nickname").clear()
wd.find_element_by_name("nickname").send_keys(group.nickname)
wd.find_element_by_name("title").click()
wd.find_element_by_name("title").clear()
wd.find_element_by_name("title").send_keys(group.title)
wd.find_element_by_name("company").click()
wd.find_element_by_name("company").clear()
wd.find_element_by_name("company").send_keys(group.company)
wd.find_element_by_name("address").click()
wd.find_element_by_name("address").click()
wd.find_element_by_name("address").clear()
wd.find_element_by_name("address").send_keys(group.address)
wd.find_element_by_name("home").click()
wd.find_element_by_name("home").clear()
wd.find_element_by_name("home").send_keys(group.home_tel)
wd.find_element_by_name("mobile").click()
wd.find_element_by_name("mobile").clear()
wd.find_element_by_name("mobile").send_keys(group.mobile_tel)
wd.find_element_by_name("work").click()
wd.find_element_by_name("work").clear()
wd.find_element_by_name("work").send_keys(group.work_tel)
wd.find_element_by_name("fax").click()
wd.find_element_by_name("fax").clear()
wd.find_element_by_name("fax").send_keys(group.fax)
wd.find_element_by_name("email").click()
wd.find_element_by_name("email").clear()
wd.find_element_by_name("email").send_keys(group.email)
wd.find_element_by_name("email2").click()
wd.find_element_by_name("email2").clear()
wd.find_element_by_name("email2").send_keys(group.email2)
wd.find_element_by_name("email3").click()
wd.find_element_by_name("email3").click()
wd.find_element_by_name("email3").clear()
wd.find_element_by_name("email3").send_keys(group.email3)
wd.find_element_by_name("homepage").click()
wd.find_element_by_name("homepage").clear()
wd.find_element_by_name("homepage").send_keys(group.homepage)
if not wd.find_element_by_xpath("//div[@id='content']/form/select[1]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[1]//option[5]").click()
if not wd.find_element_by_xpath("//div[@id='content']/form/select[2]//option[4]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[2]//option[4]").click()
wd.find_element_by_name("byear").click()
wd.find_element_by_name("byear").clear()
wd.find_element_by_name("byear").send_keys(group.byear)
if not wd.find_element_by_xpath("//div[@id='content']/form/select[3]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[3]//option[5]").click()
if not wd.find_element_by_xpath("//div[@id='content']/form/select[4]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[4]//option[5]").click()
wd.find_element_by_name("ayear").click()
wd.find_element_by_name("ayear").clear()
wd.find_element_by_name("ayear").send_keys(group.ayear)
wd.find_element_by_name("address2").click()
wd.find_element_by_name("address2").clear()
wd.find_element_by_name("address2").send_keys(group.address2)
wd.find_element_by_name("notes").click()
wd.find_element_by_name("notes").clear()
wd.find_element_by_name("notes").send_keys(group.notes)
# enter new contact
wd.find_element_by_xpath("//div[@id='content']/form/input[21]").click()
self.contact_cache = None
def delete_first_contact(self):
self.delete_contact_by_index(0)
def delete_contact_by_index(self, index):
wd = self.app.wd
#select contact by index
wd.find_elements_by_name("selected[]")[index].click()
#submit deletion
wd.find_element_by_xpath("//div[@id='content']/form[2]/div[2]/input").click()
#close the dialog window
wd.switch_to_alert().accept()
self.contact_cache = None
def delete_contact(self, contact):
wd = self.app.wd
#select contact by id
self.select_contact_by_name(contact.firstname, contact.lastname)
#submit deletion
wd.find_element_by_xpath("//input[@value='Delete']").click()
#close the dialog window
wd.switch_to_alert().accept()
self.contact_cache = None
def select_contact_by_name(self, firstname, lastname):
wd = self.app.wd
wd.find_element_by_xpath("//tbody//tr//input[@title = 'Select (%s %s)']" % (firstname, lastname)).click()
def select_contact_by_id(self, id):
wd = self.app.wd
wd.find_element_by_id("%s" % id).click()
def modify_first_contact(self, contact):
wd = self.app.wd
wd.modify_contact_by_index(0, contact)
def edit_contact(self):
wd = self.app.wd
wd.find_element_by_xpath("//table[@id='maintable']/tbody/tr[2]/td[8]/a/img").click()
def edit_contact_by_id(self, id):
wd = self.app.wd
wd.find_element_by_css_selector('input[value="id"]').click()
def modify_contact_by_index(self, group):
wd = self.app.wd
#select contact editing
self.edit_contact()
#editing contact form
wd.find_element_by_name("firstname").click()
wd.find_element_by_name("firstname").clear()
wd.find_element_by_name("firstname").send_keys(group.firstname)
wd.find_element_by_name("middlename").click()
wd.find_element_by_name("middlename").clear()
wd.find_element_by_name("middlename").send_keys(group.middlename)
wd.find_element_by_name("lastname").click()
wd.find_element_by_name("lastname").clear()
wd.find_element_by_name("lastname").send_keys(group.lastname)
wd.find_element_by_name("nickname").click()
wd.find_element_by_name("nickname").clear()
wd.find_element_by_name("nickname").send_keys(group.nickname)
wd.find_element_by_name("title").click()
wd.find_element_by_name("title").clear()
wd.find_element_by_name("title").send_keys(group.title)
wd.find_element_by_name("company").click()
wd.find_element_by_name("company").clear()
wd.find_element_by_name("company").send_keys(group.company)
wd.find_element_by_name("address").click()
wd.find_element_by_name("address").click()
wd.find_element_by_name("address").clear()
wd.find_element_by_name("address").send_keys(group.address)
wd.find_element_by_name("home").click()
wd.find_element_by_name("home").clear()
wd.find_element_by_name("home").send_keys(group.home_tel)
wd.find_element_by_name("mobile").click()
wd.find_element_by_name("mobile").clear()
wd.find_element_by_name("mobile").send_keys(group.mobile_tel)
wd.find_element_by_name("work").click()
wd.find_element_by_name("work").clear()
wd.find_element_by_name("work").send_keys(group.work_tel)
wd.find_element_by_name("fax").click()
wd.find_element_by_name("fax").clear()
wd.find_element_by_name("fax").send_keys(group.fax)
wd.find_element_by_name("email").click()
wd.find_element_by_name("email").clear()
wd.find_element_by_name("email").send_keys(group.email)
wd.find_element_by_name("email2").click()
wd.find_element_by_name("email2").clear()
wd.find_element_by_name("email2").send_keys(group.email2)
wd.find_element_by_name("email3").click()
wd.find_element_by_name("email3").click()
wd.find_element_by_name("email3").clear()
wd.find_element_by_name("email3").send_keys(group.email3)
wd.find_element_by_name("homepage").click()
wd.find_element_by_name("homepage").clear()
wd.find_element_by_name("homepage").send_keys(group.homepage)
if not wd.find_element_by_xpath("//div[@id='content']/form/select[1]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[1]//option[5]").click()
if not wd.find_element_by_xpath("//div[@id='content']/form/select[2]//option[4]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[2]//option[4]").click()
wd.find_element_by_name("byear").click()
wd.find_element_by_name("byear").clear()
wd.find_element_by_name("byear").send_keys(group.byear)
if not wd.find_element_by_xpath("//div[@id='content']/form/select[3]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[3]//option[5]").click()
if not wd.find_element_by_xpath("//div[@id='content']/form/select[4]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[4]//option[5]").click()
wd.find_element_by_name("ayear").click()
wd.find_element_by_name("ayear").clear()
wd.find_element_by_name("ayear").send_keys(group.ayear)
wd.find_element_by_name("address2").click()
wd.find_element_by_name("address2").clear()
wd.find_element_by_name("address2").send_keys(group.address2)
wd.find_element_by_name("notes").click()
wd.find_element_by_name("notes").clear()
wd.find_element_by_name("notes").send_keys(group.notes)
#submit contact edition
wd.find_element_by_name("update").click()
self.contact_cache = None
def modify_contact(self, random_contact, contact):
wd = self.app.wd
#select contact editing
self.edit_contact_by_name(contact.firstname, contact.lastname)
#editing contact form
wd.find_element_by_name("firstname").click()
wd.find_element_by_name("firstname").clear()
wd.find_element_by_name("firstname").send_keys(contact.firstname)
wd.find_element_by_name("middlename").click()
wd.find_element_by_name("middlename").clear()
wd.find_element_by_name("middlename").send_keys(contact.middlename)
wd.find_element_by_name("lastname").click()
wd.find_element_by_name("lastname").clear()
wd.find_element_by_name("lastname").send_keys(contact.lastname)
wd.find_element_by_name("nickname").click()
wd.find_element_by_name("nickname").clear()
wd.find_element_by_name("nickname").send_keys(contact.nickname)
wd.find_element_by_name("title").click()
wd.find_element_by_name("title").clear()
wd.find_element_by_name("title").send_keys(contact.title)
wd.find_element_by_name("company").click()
wd.find_element_by_name("company").clear()
wd.find_element_by_name("company").send_keys(contact.company)
wd.find_element_by_name("address").click()
wd.find_element_by_name("address").click()
wd.find_element_by_name("address").clear()
wd.find_element_by_name("address").send_keys(contact.address)
wd.find_element_by_name("home").click()
wd.find_element_by_name("home").clear()
wd.find_element_by_name("home").send_keys(contact.home_tel)
wd.find_element_by_name("mobile").click()
wd.find_element_by_name("mobile").clear()
wd.find_element_by_name("mobile").send_keys(contact.mobile_tel)
wd.find_element_by_name("work").click()
wd.find_element_by_name("work").clear()
wd.find_element_by_name("work").send_keys(contact.work_tel)
wd.find_element_by_name("fax").click()
wd.find_element_by_name("fax").clear()
wd.find_element_by_name("fax").send_keys(contact.fax)
wd.find_element_by_name("email").click()
wd.find_element_by_name("email").clear()
wd.find_element_by_name("email").send_keys(contact.email)
wd.find_element_by_name("email2").click()
wd.find_element_by_name("email2").clear()
wd.find_element_by_name("email2").send_keys(contact.email2)
wd.find_element_by_name("email3").click()
wd.find_element_by_name("email3").click()
wd.find_element_by_name("email3").clear()
wd.find_element_by_name("email3").send_keys(contact.email3)
wd.find_element_by_name("homepage").click()
wd.find_element_by_name("homepage").clear()
wd.find_element_by_name("homepage").send_keys(contact.homepage)
if not wd.find_element_by_xpath("//div[@id='content']/form/select[1]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[1]//option[5]").click()
if not wd.find_element_by_xpath("//div[@id='content']/form/select[2]//option[4]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[2]//option[4]").click()
wd.find_element_by_name("byear").click()
wd.find_element_by_name("byear").clear()
wd.find_element_by_name("byear").send_keys(contact.byear)
if not wd.find_element_by_xpath("//div[@id='content']/form/select[3]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[3]//option[5]").click()
if not wd.find_element_by_xpath("//div[@id='content']/form/select[4]//option[5]").is_selected():
wd.find_element_by_xpath("//div[@id='content']/form/select[4]//option[5]").click()
wd.find_element_by_name("ayear").click()
wd.find_element_by_name("ayear").clear()
wd.find_element_by_name("ayear").send_keys(contact.ayear)
wd.find_element_by_name("address2").click()
wd.find_element_by_name("address2").clear()
wd.find_element_by_name("address2").send_keys(contact.address2)
wd.find_element_by_name("notes").click()
wd.find_element_by_name("notes").clear()
wd.find_element_by_name("notes").send_keys(contact.notes)
#submit contact edition
wd.find_element_by_name("update").click()
self.contact_cache = None
def edit_contact_by_name(self, firstname, lastname):
wd = self.app.wd
wd.find_element_by_xpath("//tbody//input[@title = 'Select (%s %s)']//ancestor::tr//a[contains(@href, 'edit.php')]" % (firstname, lastname)).click()
def count(self):
wd = self.app.wd
return len(wd.find_elements_by_name("selected[]"))
def contacts_page_is_opened(self):
wd = self.app.wd
if not (wd.current_url.endswith("/index.php") and len(wd.find_elements_by_xpath("//div[@id='content']/form[2]/div[1]/input")) > 0):
wd.find_element_by_link_text("home").click()
contact_cache = None
def get_contact_list(self):
if self.contact_cache is None:
wd = self.app.wd
self.contact_cache = []
for row in wd.find_elements_by_name("entry"):
cells = row.find_elements_by_tag_name("td")
first = cells[2].text
last = cells[1].text
id = cells[0].find_element_by_name("selected[]").get_attribute("value")
all_phones = cells[5].text
address = cells[3].text
all_emails = cells[4].text
self.contact_cache.append(Contact(firstname=first, lastname=last, id=id, all_phones_from_home_page=all_phones,
address=address, all_emails_from_home_page=all_emails))
return list(self.contact_cache)
def open_contact_to_edit_by_index(self, index):
wd = self.app.wd
self.app.open_home_page()
row = wd.find_elements_by_name("entry")[index]
cell = row.find_elements_by_tag_name("td")[7]
cell.find_element_by_tag_name("a").click()
def open_contact_view_by_index(self, index):
wd = self.app.wd
self.app.open_home_page()
row = wd.find_elements_by_name("entry")[index]
cell = row.find_elements_by_tag_name("td")[6]
cell.find_element_by_tag_name("a").click()
def get_contact_info_from_edit_page(self, index):
wd = self.app.wd
self.open_contact_to_edit_by_index(index)
firstname = wd.find_element_by_name("firstname").get_attribute("value")
lastname = wd.find_element_by_name("lastname").get_attribute("value")
id = wd.find_element_by_name("id").get_attribute("value")
home_tel = wd.find_element_by_name("home").get_attribute("value")
work_tel = wd.find_element_by_name("work").get_attribute("value")
mobile_tel = wd.find_element_by_name("mobile").get_attribute("value")
secondary_tel = wd.find_element_by_name("phone2").get_attribute("value")
address = wd.find_element_by_name("address").get_attribute("value")
email = wd.find_element_by_name("email").get_attribute("value")
email2 = wd.find_element_by_name("email2").get_attribute("value")
email3 = wd.find_element_by_name("email3").get_attribute("value")
return Contact(firstname=firstname, lastname=lastname, id=id, home_tel=home_tel,
mobile_tel=mobile_tel, work_tel=work_tel, secondary_tel=secondary_tel,
address=address, email=email, email2=email2, email3=email3)
def get_contact_from_view_page(self, index):
wd = self.app.wd
self.open_contact_view_by_index(index)
text = wd.find_element_by_id("content").text
home_tel = re.search("H: (.*)", text).group(1)
work_tel = re.search("W: (.*)", text).group(1)
mobile_tel = re.search("M: (.*)", text).group(1)
secondary_tel = re.search("P: (.*)", text).group(1)
return Contact(home_tel=home_tel, mobile_tel=mobile_tel, work_tel=work_tel, secondary_tel=secondary_tel)
def get_emails_ui(self):
wd = self.app.wd
all_emails = []
for contact in wd.find_elements_by_name("entry"):
cells = contact.find_elements_by_tag_name("td")
email = cells[4].text
if email != '':
all_emails.append(email.split("\n"))
return all_emails
def get_phones_ui(self):
wd = self.app.wd
all_phones = []
for contact in wd.find_elements_by_name("entry"):
cells = contact.find_elements_by_tag_name("td")
phone = cells[5].text
if phone != '':
all_phones.append(phone.split("\n"))
return all_phones
def clear(self, phone):
return re.sub("[() -]", "", phone)
def merge_phones_like_on_home_page(self, contact):
return "\n".join(filter(lambda x: x != "",
(map(lambda x: self.clear(x),
filter(lambda x: x is not None,
[contact.home_tel, contact.work_tel, contact.mobile_tel,
contact.secondary_tel])))))
def merge_emails_like_on_home_page(self, contact):
return "\n".join(filter(lambda x: x != "",
filter(lambda x: x is not None,
[contact.email, contact.email2, contact.email3])))
| 48.849515
| 155
| 0.650949
| 2,826
| 20,126
| 4.290163
| 0.055202
| 0.115308
| 0.244474
| 0.278373
| 0.839492
| 0.824398
| 0.789508
| 0.758166
| 0.750742
| 0.731772
| 0
| 0.00762
| 0.198003
| 20,126
| 412
| 156
| 48.849515
| 0.74351
| 0.015105
| 0
| 0.65407
| 0
| 0.002907
| 0.142684
| 0.069524
| 0
| 0
| 0
| 0
| 0
| 1
| 0.072674
| false
| 0
| 0.005814
| 0.008721
| 0.110465
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
ba6c1eccfe2773b546da973d89a5596299989c7e
| 152
|
py
|
Python
|
src/barsxml/tests/xml_dbf_travma_config.py
|
SLikhachev/barsxml
|
cd6a54f72dba826a508d8e9c2c65a7f14ae7f865
|
[
"BSD-2-Clause"
] | null | null | null |
src/barsxml/tests/xml_dbf_travma_config.py
|
SLikhachev/barsxml
|
cd6a54f72dba826a508d8e9c2c65a7f14ae7f865
|
[
"BSD-2-Clause"
] | null | null | null |
src/barsxml/tests/xml_dbf_travma_config.py
|
SLikhachev/barsxml
|
cd6a54f72dba826a508d8e9c2c65a7f14ae7f865
|
[
"BSD-2-Clause"
] | null | null | null |
from barsxml.tests.xml_dbf_config import *
from barsxml.tests.xml_dbf_config import tests_dir
RR_DIR = str(tests_dir / 'data' / 'import' / 'travma')
| 21.714286
| 54
| 0.756579
| 24
| 152
| 4.5
| 0.5
| 0.203704
| 0.296296
| 0.351852
| 0.62963
| 0.62963
| 0.62963
| 0
| 0
| 0
| 0
| 0
| 0.131579
| 152
| 6
| 55
| 25.333333
| 0.818182
| 0
| 0
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0
| 7
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baa3afa7c4506530ffb7fed611212ba0ea1d842c
| 40,454
|
py
|
Python
|
sdk/python/pulumi_gcp/vpcaccess/connector.py
|
la3mmchen/pulumi-gcp
|
0e3c6fecd062dff78a4fd95b7ebd5ce4492ad1ea
|
[
"ECL-2.0",
"Apache-2.0"
] | 121
|
2018-06-18T19:16:42.000Z
|
2022-03-31T06:06:48.000Z
|
sdk/python/pulumi_gcp/vpcaccess/connector.py
|
la3mmchen/pulumi-gcp
|
0e3c6fecd062dff78a4fd95b7ebd5ce4492ad1ea
|
[
"ECL-2.0",
"Apache-2.0"
] | 492
|
2018-06-22T19:41:03.000Z
|
2022-03-31T15:33:53.000Z
|
sdk/python/pulumi_gcp/vpcaccess/connector.py
|
la3mmchen/pulumi-gcp
|
0e3c6fecd062dff78a4fd95b7ebd5ce4492ad1ea
|
[
"ECL-2.0",
"Apache-2.0"
] | 43
|
2018-06-19T01:43:13.000Z
|
2022-03-23T22:43:37.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
from ._inputs import *
__all__ = ['ConnectorArgs', 'Connector']
@pulumi.input_type
class ConnectorArgs:
def __init__(__self__, *,
ip_cidr_range: Optional[pulumi.Input[str]] = None,
machine_type: Optional[pulumi.Input[str]] = None,
max_instances: Optional[pulumi.Input[int]] = None,
max_throughput: Optional[pulumi.Input[int]] = None,
min_instances: Optional[pulumi.Input[int]] = None,
min_throughput: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
network: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
subnet: Optional[pulumi.Input['ConnectorSubnetArgs']] = None):
"""
The set of arguments for constructing a Connector resource.
:param pulumi.Input[str] ip_cidr_range: The range of internal addresses that follows RFC 4632 notation. Example: `10.132.0.0/28`.
:param pulumi.Input[str] machine_type: Machine type of VM Instance underlying connector. Default is e2-micro
:param pulumi.Input[int] max_instances: Maximum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] max_throughput: Maximum throughput of the connector in Mbps, must be greater than `min_throughput`. Default is 300.
:param pulumi.Input[int] min_instances: Minimum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] min_throughput: Minimum throughput of the connector in Mbps. Default and min is 200.
:param pulumi.Input[str] name: Subnet name (relative, not fully qualified). E.g. if the full subnet selfLink is
https://compute.googleapis.com/compute/v1/projects/{project}/regions/{region}/subnetworks/{subnetName} the correct input for this field would be {subnetName}"
:param pulumi.Input[str] network: Name of the VPC network. Required if `ip_cidr_range` is set.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] region: Region where the VPC Access connector resides. If it is not provided, the provider region is used.
:param pulumi.Input['ConnectorSubnetArgs'] subnet: The subnet in which to house the connector
Structure is documented below.
"""
if ip_cidr_range is not None:
pulumi.set(__self__, "ip_cidr_range", ip_cidr_range)
if machine_type is not None:
pulumi.set(__self__, "machine_type", machine_type)
if max_instances is not None:
pulumi.set(__self__, "max_instances", max_instances)
if max_throughput is not None:
pulumi.set(__self__, "max_throughput", max_throughput)
if min_instances is not None:
pulumi.set(__self__, "min_instances", min_instances)
if min_throughput is not None:
pulumi.set(__self__, "min_throughput", min_throughput)
if name is not None:
pulumi.set(__self__, "name", name)
if network is not None:
pulumi.set(__self__, "network", network)
if project is not None:
pulumi.set(__self__, "project", project)
if region is not None:
pulumi.set(__self__, "region", region)
if subnet is not None:
pulumi.set(__self__, "subnet", subnet)
@property
@pulumi.getter(name="ipCidrRange")
def ip_cidr_range(self) -> Optional[pulumi.Input[str]]:
"""
The range of internal addresses that follows RFC 4632 notation. Example: `10.132.0.0/28`.
"""
return pulumi.get(self, "ip_cidr_range")
@ip_cidr_range.setter
def ip_cidr_range(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ip_cidr_range", value)
@property
@pulumi.getter(name="machineType")
def machine_type(self) -> Optional[pulumi.Input[str]]:
"""
Machine type of VM Instance underlying connector. Default is e2-micro
"""
return pulumi.get(self, "machine_type")
@machine_type.setter
def machine_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "machine_type", value)
@property
@pulumi.getter(name="maxInstances")
def max_instances(self) -> Optional[pulumi.Input[int]]:
"""
Maximum value of instances in autoscaling group underlying the connector.
"""
return pulumi.get(self, "max_instances")
@max_instances.setter
def max_instances(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_instances", value)
@property
@pulumi.getter(name="maxThroughput")
def max_throughput(self) -> Optional[pulumi.Input[int]]:
"""
Maximum throughput of the connector in Mbps, must be greater than `min_throughput`. Default is 300.
"""
return pulumi.get(self, "max_throughput")
@max_throughput.setter
def max_throughput(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_throughput", value)
@property
@pulumi.getter(name="minInstances")
def min_instances(self) -> Optional[pulumi.Input[int]]:
"""
Minimum value of instances in autoscaling group underlying the connector.
"""
return pulumi.get(self, "min_instances")
@min_instances.setter
def min_instances(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_instances", value)
@property
@pulumi.getter(name="minThroughput")
def min_throughput(self) -> Optional[pulumi.Input[int]]:
"""
Minimum throughput of the connector in Mbps. Default and min is 200.
"""
return pulumi.get(self, "min_throughput")
@min_throughput.setter
def min_throughput(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_throughput", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Subnet name (relative, not fully qualified). E.g. if the full subnet selfLink is
https://compute.googleapis.com/compute/v1/projects/{project}/regions/{region}/subnetworks/{subnetName} the correct input for this field would be {subnetName}"
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def network(self) -> Optional[pulumi.Input[str]]:
"""
Name of the VPC network. Required if `ip_cidr_range` is set.
"""
return pulumi.get(self, "network")
@network.setter
def network(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "network", value)
@property
@pulumi.getter
def project(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@project.setter
def project(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "project", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[str]]:
"""
Region where the VPC Access connector resides. If it is not provided, the provider region is used.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter
def subnet(self) -> Optional[pulumi.Input['ConnectorSubnetArgs']]:
"""
The subnet in which to house the connector
Structure is documented below.
"""
return pulumi.get(self, "subnet")
@subnet.setter
def subnet(self, value: Optional[pulumi.Input['ConnectorSubnetArgs']]):
pulumi.set(self, "subnet", value)
@pulumi.input_type
class _ConnectorState:
def __init__(__self__, *,
ip_cidr_range: Optional[pulumi.Input[str]] = None,
machine_type: Optional[pulumi.Input[str]] = None,
max_instances: Optional[pulumi.Input[int]] = None,
max_throughput: Optional[pulumi.Input[int]] = None,
min_instances: Optional[pulumi.Input[int]] = None,
min_throughput: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
network: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
self_link: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
subnet: Optional[pulumi.Input['ConnectorSubnetArgs']] = None):
"""
Input properties used for looking up and filtering Connector resources.
:param pulumi.Input[str] ip_cidr_range: The range of internal addresses that follows RFC 4632 notation. Example: `10.132.0.0/28`.
:param pulumi.Input[str] machine_type: Machine type of VM Instance underlying connector. Default is e2-micro
:param pulumi.Input[int] max_instances: Maximum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] max_throughput: Maximum throughput of the connector in Mbps, must be greater than `min_throughput`. Default is 300.
:param pulumi.Input[int] min_instances: Minimum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] min_throughput: Minimum throughput of the connector in Mbps. Default and min is 200.
:param pulumi.Input[str] name: Subnet name (relative, not fully qualified). E.g. if the full subnet selfLink is
https://compute.googleapis.com/compute/v1/projects/{project}/regions/{region}/subnetworks/{subnetName} the correct input for this field would be {subnetName}"
:param pulumi.Input[str] network: Name of the VPC network. Required if `ip_cidr_range` is set.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] region: Region where the VPC Access connector resides. If it is not provided, the provider region is used.
:param pulumi.Input[str] self_link: The fully qualified name of this VPC connector
:param pulumi.Input[str] state: State of the VPC access connector.
:param pulumi.Input['ConnectorSubnetArgs'] subnet: The subnet in which to house the connector
Structure is documented below.
"""
if ip_cidr_range is not None:
pulumi.set(__self__, "ip_cidr_range", ip_cidr_range)
if machine_type is not None:
pulumi.set(__self__, "machine_type", machine_type)
if max_instances is not None:
pulumi.set(__self__, "max_instances", max_instances)
if max_throughput is not None:
pulumi.set(__self__, "max_throughput", max_throughput)
if min_instances is not None:
pulumi.set(__self__, "min_instances", min_instances)
if min_throughput is not None:
pulumi.set(__self__, "min_throughput", min_throughput)
if name is not None:
pulumi.set(__self__, "name", name)
if network is not None:
pulumi.set(__self__, "network", network)
if project is not None:
pulumi.set(__self__, "project", project)
if region is not None:
pulumi.set(__self__, "region", region)
if self_link is not None:
pulumi.set(__self__, "self_link", self_link)
if state is not None:
pulumi.set(__self__, "state", state)
if subnet is not None:
pulumi.set(__self__, "subnet", subnet)
@property
@pulumi.getter(name="ipCidrRange")
def ip_cidr_range(self) -> Optional[pulumi.Input[str]]:
"""
The range of internal addresses that follows RFC 4632 notation. Example: `10.132.0.0/28`.
"""
return pulumi.get(self, "ip_cidr_range")
@ip_cidr_range.setter
def ip_cidr_range(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ip_cidr_range", value)
@property
@pulumi.getter(name="machineType")
def machine_type(self) -> Optional[pulumi.Input[str]]:
"""
Machine type of VM Instance underlying connector. Default is e2-micro
"""
return pulumi.get(self, "machine_type")
@machine_type.setter
def machine_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "machine_type", value)
@property
@pulumi.getter(name="maxInstances")
def max_instances(self) -> Optional[pulumi.Input[int]]:
"""
Maximum value of instances in autoscaling group underlying the connector.
"""
return pulumi.get(self, "max_instances")
@max_instances.setter
def max_instances(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_instances", value)
@property
@pulumi.getter(name="maxThroughput")
def max_throughput(self) -> Optional[pulumi.Input[int]]:
"""
Maximum throughput of the connector in Mbps, must be greater than `min_throughput`. Default is 300.
"""
return pulumi.get(self, "max_throughput")
@max_throughput.setter
def max_throughput(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_throughput", value)
@property
@pulumi.getter(name="minInstances")
def min_instances(self) -> Optional[pulumi.Input[int]]:
"""
Minimum value of instances in autoscaling group underlying the connector.
"""
return pulumi.get(self, "min_instances")
@min_instances.setter
def min_instances(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_instances", value)
@property
@pulumi.getter(name="minThroughput")
def min_throughput(self) -> Optional[pulumi.Input[int]]:
"""
Minimum throughput of the connector in Mbps. Default and min is 200.
"""
return pulumi.get(self, "min_throughput")
@min_throughput.setter
def min_throughput(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_throughput", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Subnet name (relative, not fully qualified). E.g. if the full subnet selfLink is
https://compute.googleapis.com/compute/v1/projects/{project}/regions/{region}/subnetworks/{subnetName} the correct input for this field would be {subnetName}"
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def network(self) -> Optional[pulumi.Input[str]]:
"""
Name of the VPC network. Required if `ip_cidr_range` is set.
"""
return pulumi.get(self, "network")
@network.setter
def network(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "network", value)
@property
@pulumi.getter
def project(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@project.setter
def project(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "project", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[str]]:
"""
Region where the VPC Access connector resides. If it is not provided, the provider region is used.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter(name="selfLink")
def self_link(self) -> Optional[pulumi.Input[str]]:
"""
The fully qualified name of this VPC connector
"""
return pulumi.get(self, "self_link")
@self_link.setter
def self_link(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "self_link", value)
@property
@pulumi.getter
def state(self) -> Optional[pulumi.Input[str]]:
"""
State of the VPC access connector.
"""
return pulumi.get(self, "state")
@state.setter
def state(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "state", value)
@property
@pulumi.getter
def subnet(self) -> Optional[pulumi.Input['ConnectorSubnetArgs']]:
"""
The subnet in which to house the connector
Structure is documented below.
"""
return pulumi.get(self, "subnet")
@subnet.setter
def subnet(self, value: Optional[pulumi.Input['ConnectorSubnetArgs']]):
pulumi.set(self, "subnet", value)
class Connector(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
ip_cidr_range: Optional[pulumi.Input[str]] = None,
machine_type: Optional[pulumi.Input[str]] = None,
max_instances: Optional[pulumi.Input[int]] = None,
max_throughput: Optional[pulumi.Input[int]] = None,
min_instances: Optional[pulumi.Input[int]] = None,
min_throughput: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
network: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
subnet: Optional[pulumi.Input[pulumi.InputType['ConnectorSubnetArgs']]] = None,
__props__=None):
"""
Serverless VPC Access connector resource.
To get more information about Connector, see:
* [API documentation](https://cloud.google.com/vpc/docs/reference/vpcaccess/rest/v1/projects.locations.connectors)
* How-to Guides
* [Configuring Serverless VPC Access](https://cloud.google.com/vpc/docs/configure-serverless-vpc-access)
## Example Usage
### VPC Access Connector
```python
import pulumi
import pulumi_gcp as gcp
connector = gcp.vpcaccess.Connector("connector",
ip_cidr_range="10.8.0.0/28",
network="default")
```
### VPC Access Connector Shared VPC
```python
import pulumi
import pulumi_gcp as gcp
custom_test_network = gcp.compute.Network("customTestNetwork", auto_create_subnetworks=False,
opts=pulumi.ResourceOptions(provider=google_beta))
custom_test_subnetwork = gcp.compute.Subnetwork("customTestSubnetwork",
ip_cidr_range="10.2.0.0/28",
region="us-central1",
network=custom_test_network.id,
opts=pulumi.ResourceOptions(provider=google_beta))
connector = gcp.vpcaccess.Connector("connector",
subnet=gcp.vpcaccess.ConnectorSubnetArgs(
name=custom_test_subnetwork.name,
),
machine_type="e2-standard-4",
opts=pulumi.ResourceOptions(provider=google_beta))
```
### Cloudrun VPC Access Connector
```python
import pulumi
import pulumi_gcp as gcp
vpcaccess_api = gcp.projects.Service("vpcaccessApi",
service="vpcaccess.googleapis.com",
disable_on_destroy=False,
opts=pulumi.ResourceOptions(provider=google_beta))
# VPC
default = gcp.compute.Network("default", auto_create_subnetworks=False,
opts=pulumi.ResourceOptions(provider=google_beta))
# VPC access connector
connector = gcp.vpcaccess.Connector("connector",
region="us-west1",
ip_cidr_range="10.8.0.0/28",
network=default.name,
opts=pulumi.ResourceOptions(provider=google_beta,
depends_on=[vpcaccess_api]))
# Cloud Router
router = gcp.compute.Router("router",
region="us-west1",
network=default.id,
opts=pulumi.ResourceOptions(provider=google_beta))
# NAT configuration
router_nat = gcp.compute.RouterNat("routerNat",
region="us-west1",
router=router.name,
source_subnetwork_ip_ranges_to_nat="ALL_SUBNETWORKS_ALL_IP_RANGES",
nat_ip_allocate_option="AUTO_ONLY",
opts=pulumi.ResourceOptions(provider=google_beta))
# Cloud Run service
gcr_service = gcp.cloudrun.Service("gcrService",
location="us-west1",
template=gcp.cloudrun.ServiceTemplateArgs(
spec=gcp.cloudrun.ServiceTemplateSpecArgs(
containers=[gcp.cloudrun.ServiceTemplateSpecContainerArgs(
image="us-docker.pkg.dev/cloudrun/container/hello",
resources=gcp.cloudrun.ServiceTemplateSpecContainerResourcesArgs(
limits={
"cpu": "1000m",
"memory": "512M",
},
),
)],
),
metadata=gcp.cloudrun.ServiceTemplateMetadataArgs(
annotations={
"autoscaling.knative.dev/maxScale": "5",
"run.googleapis.com/vpc-access-connector": connector.name,
"run.googleapis.com/vpc-access-egress": "all",
},
),
),
autogenerate_revision_name=True,
opts=pulumi.ResourceOptions(provider=google_beta))
```
## Import
Connector can be imported using any of these accepted formats
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default projects/{{project}}/locations/{{region}}/connectors/{{name}}
```
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default {{project}}/{{region}}/{{name}}
```
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default {{region}}/{{name}}
```
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default {{name}}
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] ip_cidr_range: The range of internal addresses that follows RFC 4632 notation. Example: `10.132.0.0/28`.
:param pulumi.Input[str] machine_type: Machine type of VM Instance underlying connector. Default is e2-micro
:param pulumi.Input[int] max_instances: Maximum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] max_throughput: Maximum throughput of the connector in Mbps, must be greater than `min_throughput`. Default is 300.
:param pulumi.Input[int] min_instances: Minimum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] min_throughput: Minimum throughput of the connector in Mbps. Default and min is 200.
:param pulumi.Input[str] name: Subnet name (relative, not fully qualified). E.g. if the full subnet selfLink is
https://compute.googleapis.com/compute/v1/projects/{project}/regions/{region}/subnetworks/{subnetName} the correct input for this field would be {subnetName}"
:param pulumi.Input[str] network: Name of the VPC network. Required if `ip_cidr_range` is set.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] region: Region where the VPC Access connector resides. If it is not provided, the provider region is used.
:param pulumi.Input[pulumi.InputType['ConnectorSubnetArgs']] subnet: The subnet in which to house the connector
Structure is documented below.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[ConnectorArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Serverless VPC Access connector resource.
To get more information about Connector, see:
* [API documentation](https://cloud.google.com/vpc/docs/reference/vpcaccess/rest/v1/projects.locations.connectors)
* How-to Guides
* [Configuring Serverless VPC Access](https://cloud.google.com/vpc/docs/configure-serverless-vpc-access)
## Example Usage
### VPC Access Connector
```python
import pulumi
import pulumi_gcp as gcp
connector = gcp.vpcaccess.Connector("connector",
ip_cidr_range="10.8.0.0/28",
network="default")
```
### VPC Access Connector Shared VPC
```python
import pulumi
import pulumi_gcp as gcp
custom_test_network = gcp.compute.Network("customTestNetwork", auto_create_subnetworks=False,
opts=pulumi.ResourceOptions(provider=google_beta))
custom_test_subnetwork = gcp.compute.Subnetwork("customTestSubnetwork",
ip_cidr_range="10.2.0.0/28",
region="us-central1",
network=custom_test_network.id,
opts=pulumi.ResourceOptions(provider=google_beta))
connector = gcp.vpcaccess.Connector("connector",
subnet=gcp.vpcaccess.ConnectorSubnetArgs(
name=custom_test_subnetwork.name,
),
machine_type="e2-standard-4",
opts=pulumi.ResourceOptions(provider=google_beta))
```
### Cloudrun VPC Access Connector
```python
import pulumi
import pulumi_gcp as gcp
vpcaccess_api = gcp.projects.Service("vpcaccessApi",
service="vpcaccess.googleapis.com",
disable_on_destroy=False,
opts=pulumi.ResourceOptions(provider=google_beta))
# VPC
default = gcp.compute.Network("default", auto_create_subnetworks=False,
opts=pulumi.ResourceOptions(provider=google_beta))
# VPC access connector
connector = gcp.vpcaccess.Connector("connector",
region="us-west1",
ip_cidr_range="10.8.0.0/28",
network=default.name,
opts=pulumi.ResourceOptions(provider=google_beta,
depends_on=[vpcaccess_api]))
# Cloud Router
router = gcp.compute.Router("router",
region="us-west1",
network=default.id,
opts=pulumi.ResourceOptions(provider=google_beta))
# NAT configuration
router_nat = gcp.compute.RouterNat("routerNat",
region="us-west1",
router=router.name,
source_subnetwork_ip_ranges_to_nat="ALL_SUBNETWORKS_ALL_IP_RANGES",
nat_ip_allocate_option="AUTO_ONLY",
opts=pulumi.ResourceOptions(provider=google_beta))
# Cloud Run service
gcr_service = gcp.cloudrun.Service("gcrService",
location="us-west1",
template=gcp.cloudrun.ServiceTemplateArgs(
spec=gcp.cloudrun.ServiceTemplateSpecArgs(
containers=[gcp.cloudrun.ServiceTemplateSpecContainerArgs(
image="us-docker.pkg.dev/cloudrun/container/hello",
resources=gcp.cloudrun.ServiceTemplateSpecContainerResourcesArgs(
limits={
"cpu": "1000m",
"memory": "512M",
},
),
)],
),
metadata=gcp.cloudrun.ServiceTemplateMetadataArgs(
annotations={
"autoscaling.knative.dev/maxScale": "5",
"run.googleapis.com/vpc-access-connector": connector.name,
"run.googleapis.com/vpc-access-egress": "all",
},
),
),
autogenerate_revision_name=True,
opts=pulumi.ResourceOptions(provider=google_beta))
```
## Import
Connector can be imported using any of these accepted formats
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default projects/{{project}}/locations/{{region}}/connectors/{{name}}
```
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default {{project}}/{{region}}/{{name}}
```
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default {{region}}/{{name}}
```
```sh
$ pulumi import gcp:vpcaccess/connector:Connector default {{name}}
```
:param str resource_name: The name of the resource.
:param ConnectorArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ConnectorArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
ip_cidr_range: Optional[pulumi.Input[str]] = None,
machine_type: Optional[pulumi.Input[str]] = None,
max_instances: Optional[pulumi.Input[int]] = None,
max_throughput: Optional[pulumi.Input[int]] = None,
min_instances: Optional[pulumi.Input[int]] = None,
min_throughput: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
network: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
subnet: Optional[pulumi.Input[pulumi.InputType['ConnectorSubnetArgs']]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ConnectorArgs.__new__(ConnectorArgs)
__props__.__dict__["ip_cidr_range"] = ip_cidr_range
__props__.__dict__["machine_type"] = machine_type
__props__.__dict__["max_instances"] = max_instances
__props__.__dict__["max_throughput"] = max_throughput
__props__.__dict__["min_instances"] = min_instances
__props__.__dict__["min_throughput"] = min_throughput
__props__.__dict__["name"] = name
__props__.__dict__["network"] = network
__props__.__dict__["project"] = project
__props__.__dict__["region"] = region
__props__.__dict__["subnet"] = subnet
__props__.__dict__["self_link"] = None
__props__.__dict__["state"] = None
super(Connector, __self__).__init__(
'gcp:vpcaccess/connector:Connector',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
ip_cidr_range: Optional[pulumi.Input[str]] = None,
machine_type: Optional[pulumi.Input[str]] = None,
max_instances: Optional[pulumi.Input[int]] = None,
max_throughput: Optional[pulumi.Input[int]] = None,
min_instances: Optional[pulumi.Input[int]] = None,
min_throughput: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
network: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
self_link: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
subnet: Optional[pulumi.Input[pulumi.InputType['ConnectorSubnetArgs']]] = None) -> 'Connector':
"""
Get an existing Connector resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] ip_cidr_range: The range of internal addresses that follows RFC 4632 notation. Example: `10.132.0.0/28`.
:param pulumi.Input[str] machine_type: Machine type of VM Instance underlying connector. Default is e2-micro
:param pulumi.Input[int] max_instances: Maximum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] max_throughput: Maximum throughput of the connector in Mbps, must be greater than `min_throughput`. Default is 300.
:param pulumi.Input[int] min_instances: Minimum value of instances in autoscaling group underlying the connector.
:param pulumi.Input[int] min_throughput: Minimum throughput of the connector in Mbps. Default and min is 200.
:param pulumi.Input[str] name: Subnet name (relative, not fully qualified). E.g. if the full subnet selfLink is
https://compute.googleapis.com/compute/v1/projects/{project}/regions/{region}/subnetworks/{subnetName} the correct input for this field would be {subnetName}"
:param pulumi.Input[str] network: Name of the VPC network. Required if `ip_cidr_range` is set.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] region: Region where the VPC Access connector resides. If it is not provided, the provider region is used.
:param pulumi.Input[str] self_link: The fully qualified name of this VPC connector
:param pulumi.Input[str] state: State of the VPC access connector.
:param pulumi.Input[pulumi.InputType['ConnectorSubnetArgs']] subnet: The subnet in which to house the connector
Structure is documented below.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _ConnectorState.__new__(_ConnectorState)
__props__.__dict__["ip_cidr_range"] = ip_cidr_range
__props__.__dict__["machine_type"] = machine_type
__props__.__dict__["max_instances"] = max_instances
__props__.__dict__["max_throughput"] = max_throughput
__props__.__dict__["min_instances"] = min_instances
__props__.__dict__["min_throughput"] = min_throughput
__props__.__dict__["name"] = name
__props__.__dict__["network"] = network
__props__.__dict__["project"] = project
__props__.__dict__["region"] = region
__props__.__dict__["self_link"] = self_link
__props__.__dict__["state"] = state
__props__.__dict__["subnet"] = subnet
return Connector(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="ipCidrRange")
def ip_cidr_range(self) -> pulumi.Output[Optional[str]]:
"""
The range of internal addresses that follows RFC 4632 notation. Example: `10.132.0.0/28`.
"""
return pulumi.get(self, "ip_cidr_range")
@property
@pulumi.getter(name="machineType")
def machine_type(self) -> pulumi.Output[Optional[str]]:
"""
Machine type of VM Instance underlying connector. Default is e2-micro
"""
return pulumi.get(self, "machine_type")
@property
@pulumi.getter(name="maxInstances")
def max_instances(self) -> pulumi.Output[int]:
"""
Maximum value of instances in autoscaling group underlying the connector.
"""
return pulumi.get(self, "max_instances")
@property
@pulumi.getter(name="maxThroughput")
def max_throughput(self) -> pulumi.Output[Optional[int]]:
"""
Maximum throughput of the connector in Mbps, must be greater than `min_throughput`. Default is 300.
"""
return pulumi.get(self, "max_throughput")
@property
@pulumi.getter(name="minInstances")
def min_instances(self) -> pulumi.Output[int]:
"""
Minimum value of instances in autoscaling group underlying the connector.
"""
return pulumi.get(self, "min_instances")
@property
@pulumi.getter(name="minThroughput")
def min_throughput(self) -> pulumi.Output[Optional[int]]:
"""
Minimum throughput of the connector in Mbps. Default and min is 200.
"""
return pulumi.get(self, "min_throughput")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Subnet name (relative, not fully qualified). E.g. if the full subnet selfLink is
https://compute.googleapis.com/compute/v1/projects/{project}/regions/{region}/subnetworks/{subnetName} the correct input for this field would be {subnetName}"
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def network(self) -> pulumi.Output[Optional[str]]:
"""
Name of the VPC network. Required if `ip_cidr_range` is set.
"""
return pulumi.get(self, "network")
@property
@pulumi.getter
def project(self) -> pulumi.Output[str]:
"""
The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@property
@pulumi.getter
def region(self) -> pulumi.Output[str]:
"""
Region where the VPC Access connector resides. If it is not provided, the provider region is used.
"""
return pulumi.get(self, "region")
@property
@pulumi.getter(name="selfLink")
def self_link(self) -> pulumi.Output[str]:
"""
The fully qualified name of this VPC connector
"""
return pulumi.get(self, "self_link")
@property
@pulumi.getter
def state(self) -> pulumi.Output[str]:
"""
State of the VPC access connector.
"""
return pulumi.get(self, "state")
@property
@pulumi.getter
def subnet(self) -> pulumi.Output[Optional['outputs.ConnectorSubnet']]:
"""
The subnet in which to house the connector
Structure is documented below.
"""
return pulumi.get(self, "subnet")
| 43.545748
| 173
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| 0.00294
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| false
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|
0
| 8
|
24093535823c9018870829d3e0c3a78ec0f25bb3
| 28,352
|
py
|
Python
|
models/load_model.py
|
scott-mao/CPD
|
953a6d1dfcafca44b9960aec17b65ac9d1ad576b
|
[
"MIT"
] | 1
|
2021-11-16T03:37:39.000Z
|
2021-11-16T03:37:39.000Z
|
models/load_model.py
|
scott-mao/CPD
|
953a6d1dfcafca44b9960aec17b65ac9d1ad576b
|
[
"MIT"
] | null | null | null |
models/load_model.py
|
scott-mao/CPD
|
953a6d1dfcafca44b9960aec17b65ac9d1ad576b
|
[
"MIT"
] | null | null | null |
import torch.nn as nn
import numpy as np
import copy
from models.cifar10.googlenet import Inception
def load_vgg_model(args, logger, model, oristate_dict):
state_dict = model.state_dict()
last_select_index = None #Conv index selected in the previous layer
cnt=0
prefix = args.rank_conv_prefix+'/rank_conv_w'
subfix = ".npy"
for name, module in model.named_modules():
name = name.replace('module.', '')
if isinstance(module, nn.Conv2d):
cnt+=1
oriweight = oristate_dict[name + '.weight']
curweight =state_dict[args.name_base+name + '.weight']
orifilter_num = oriweight.size(0)
currentfilter_num = curweight.size(0)
if orifilter_num != currentfilter_num:
cov_id = cnt
logger.info('loading rank from: ' + prefix + str(cov_id) + subfix)
rank = np.load(prefix + str(cov_id) + subfix)
select_index = np.argsort(rank)[orifilter_num-currentfilter_num:] # preserved filter id
select_index.sort()
if last_select_index is not None:
for index_i, i in enumerate(select_index):
for index_j, j in enumerate(last_select_index):
state_dict[args.name_base+name + '.weight'][index_i][index_j] = \
oristate_dict[name + '.weight'][i][j]
else:
for index_i, i in enumerate(select_index):
state_dict[args.name_base+name + '.weight'][index_i] = \
oristate_dict[name + '.weight'][i]
last_select_index = select_index
elif last_select_index is not None:
for i in range(orifilter_num):
for index_j, j in enumerate(last_select_index):
state_dict[args.name_base+name + '.weight'][i][index_j] = \
oristate_dict[name + '.weight'][i][j]
else:
state_dict[args.name_base+name + '.weight'] = oriweight
last_select_index = None
model.load_state_dict(state_dict)
def load_resnet_model(args, logger, model, oristate_dict, layer):
cfg = {
56: [9, 9, 9],
110: [18, 18, 18],
}
state_dict = model.state_dict()
current_cfg = cfg[layer]
last_select_index = None
all_conv_weight = []
prefix = args.rank_conv_prefix+'/rank_conv_w'
subfix = ".npy"
cnt=1
for layer, num in enumerate(current_cfg):
layer_name = 'layer' + str(layer + 1) + '.'
for k in range(num):
for l in range(2):
cnt+=1
cov_id=cnt
conv_name = layer_name + str(k) + '.conv' + str(l + 1)
conv_weight_name = conv_name + '.weight'
all_conv_weight.append(conv_weight_name)
oriweight = oristate_dict[conv_weight_name] # parameters from the fully-connected model
curweight =state_dict[args.name_base+conv_weight_name] #parameters from the (already) compressed model
orifilter_num = oriweight.size(0)
currentfilter_num = curweight.size(0)
if orifilter_num != currentfilter_num: #만약에 두 필터의 사이즈가 다르면 (즉, pruning 되어져 있다면) orig > curr
logger.info('loading rank from: ' + prefix + str(cov_id) + subfix)
rank = np.load(prefix + str(cov_id) + subfix)
select_index = np.argsort(rank)[orifilter_num - currentfilter_num:] # preserved filter id
select_index.sort()
if last_select_index is not None:
for index_i, i in enumerate(select_index):
for index_j, j in enumerate(last_select_index):
state_dict[args.name_base+conv_weight_name][index_i][index_j] = \
oristate_dict[conv_weight_name][i][j]
else:
for index_i, i in enumerate(select_index):
state_dict[args.name_base+conv_weight_name][index_i] = \
oristate_dict[conv_weight_name][i]
last_select_index = select_index
elif last_select_index is not None:
for index_i in range(orifilter_num):
for index_j, j in enumerate(last_select_index):
state_dict[args.name_base+conv_weight_name][index_i][index_j] = \
oristate_dict[conv_weight_name][index_i][j]
last_select_index = None
else:
state_dict[args.name_base+conv_weight_name] = oriweight
last_select_index = None
for name, module in model.named_modules():
name = name.replace('module.', '')
if isinstance(module, nn.Conv2d):
conv_name = name + '.weight'
if 'shortcut' in name:
continue
if conv_name not in all_conv_weight:
state_dict[args.name_base+conv_name] = oristate_dict[conv_name]
elif isinstance(module, nn.Linear):
state_dict[args.name_base+name + '.weight'] = oristate_dict[name + '.weight']
state_dict[args.name_base+name + '.bias'] = oristate_dict[name + '.bias']
model.load_state_dict(state_dict)
def load_google_model(args, logger, model, oristate_dict):
state_dict = model.state_dict()
filters = [
[64, 128, 32, 32],
[128, 192, 96, 64],
[192, 208, 48, 64],
[160, 224, 64, 64],
[128, 256, 64, 64],
[112, 288, 64, 64],
[256, 320, 128, 128],
[256, 320, 128, 128],
[384, 384, 128, 128]
]
#last_select_index = []
all_honey_conv_name = []
all_honey_bn_name = []
cur_last_select_index = []
cnt=0
prefix = args.rank_conv_prefix+'/rank_conv_w'
subfix = ".npy"
for name, module in model.named_modules():
name = name.replace('module.', '')
if isinstance(module, Inception):
cnt += 1
cov_id = cnt
# print(f'Inception: {cnt}')
honey_filter_channel_index = [
'.branch5x5.6',
] # the index of sketch filter and channel weight
honey_channel_index = [
'.branch1x1.0',
'.branch3x3.0',
'.branch5x5.0',
'.branch_pool.1'
] # the index of sketch channel weight
honey_filter_index = [
'.branch3x3.3',
'.branch5x5.3',
] # the index of sketch filter weight
honey_bn_index = [
'.branch3x3.4',
'.branch5x5.4',
'.branch5x5.7',
] # the index of sketch bn weight
for bn_index in honey_bn_index:
all_honey_bn_name.append(name + bn_index)
last_select_index = cur_last_select_index[:]
cur_last_select_index=[]
for weight_index in honey_channel_index: #branch에 맨 첫번째 conv
if '3x3' in weight_index:
branch_name='_n3x3'
elif '5x5' in weight_index:
branch_name='_n5x5'
elif '1x1' in weight_index:
branch_name='_n1x1'
elif 'pool' in weight_index:
branch_name='_pool_planes'
conv_name = name + weight_index + '.weight'
# print(f'{conv_name}')
all_honey_conv_name.append(name + weight_index)
oriweight = oristate_dict[conv_name]
curweight =state_dict[args.name_base+conv_name]
orifilter_num = oriweight.size(1)
currentfilter_num = curweight.size(1)
if orifilter_num != currentfilter_num:
select_index = last_select_index
else:
select_index = list(range(0, orifilter_num))
for i in range(state_dict[args.name_base+conv_name].size(0)):
for index_j, j in enumerate(select_index):
state_dict[args.name_base+conv_name][i][index_j] = \
oristate_dict[conv_name][i][j]
if branch_name=='_n1x1':
tmp_select_index = list(range(state_dict[args.name_base+conv_name].size(0)))
cur_last_select_index += tmp_select_index
if branch_name=='_pool_planes':
tmp_select_index = list(range(state_dict[args.name_base+conv_name].size(0)))
tmp_select_index = [x+filters[cov_id-2][0]+filters[cov_id-2][1]+filters[cov_id-2][2] for x in tmp_select_index]
cur_last_select_index += tmp_select_index
for weight_index in honey_filter_index:
if '3x3' in weight_index:
branch_name='_n3x3'
count = 3
elif '5x5' in weight_index:
branch_name='_n5x5'
count = 5
elif '1x1' in weight_index:
branch_name='_n1x1'
assert 'warning'
elif 'pool' in weight_index:
branch_name='_pool_planes'
assert 'warning'
conv_name = name + weight_index + '.weight'
# print(f'{conv_name}')
all_honey_conv_name.append(name + weight_index)
oriweight = oristate_dict[conv_name]
curweight =state_dict[args.name_base+conv_name]
orifilter_num = oriweight.size(0)
currentfilter_num = curweight.size(0)
if orifilter_num != currentfilter_num:
# logger.info('loading rank from: ' + prefix + str(cov_id) + branch_name + subfix)
# rank = np.load(prefix + str(cov_id) + branch_name + subfix)
logger.info('loading rank from: ' + prefix + str(1 + (7 * (cov_id - 2)) + count) + subfix)
rank = np.load(prefix + str(1 + (7 * (cov_id - 2)) + count) + subfix)
select_index = np.argsort(rank)[orifilter_num - currentfilter_num:] # preserved filter id
select_index.sort()
else:
select_index = list(range(0, orifilter_num))
for index_i, i in enumerate(select_index):
state_dict[args.name_base+conv_name][index_i] = \
oristate_dict[conv_name][i]
if branch_name=='_n3x3':
tmp_select_index = [x+filters[cov_id-2][0] for x in select_index]
cur_last_select_index += tmp_select_index
if branch_name=='_n5x5':
last_select_index=select_index
for weight_index in honey_filter_channel_index:
if '3x3' in weight_index:
branch_name='_n3x3'
elif '5x5' in weight_index:
branch_name='_n5x5'
count = 6
elif '1x1' in weight_index:
branch_name='_n1x1'
elif 'pool' in weight_index:
branch_name='_pool_planes'
conv_name = name + weight_index + '.weight'
# print(f'{conv_name}')
all_honey_conv_name.append(name + weight_index)
oriweight = oristate_dict[conv_name]
curweight = state_dict[args.name_base+conv_name]
orifilter_num = oriweight.size(1)
currentfilter_num = curweight.size(1)
if orifilter_num != currentfilter_num:
select_index = last_select_index
else:
select_index = range(0, orifilter_num)
orifilter_num = oriweight.size(0)
currentfilter_num = curweight.size(0)
select_index_1 = copy.deepcopy(select_index)
if orifilter_num != currentfilter_num:
# logger.info('loading rank from: ' + prefix + str(cov_id) + branch_name + subfix)
# rank = np.load(prefix + str(cov_id) + branch_name + subfix)
logger.info('loading rank from: ' + prefix + str(1 + (7 * (cov_id - 2)) + count) + subfix)
rank = np.load(prefix + str(1 + (7 * (cov_id - 2)) + count) + subfix)
select_index = np.argsort(rank)[orifilter_num - currentfilter_num:] # preserved filter id
select_index.sort()
else:
print(f'here')
select_index = list(range(0, orifilter_num))
if branch_name == '_n5x5':
tmp_select_index = [x+filters[cov_id-2][0]+filters[cov_id-2][1] for x in select_index]
cur_last_select_index += tmp_select_index
for index_i, i in enumerate(select_index):
for index_j, j in enumerate(select_index_1):
state_dict[args.name_base+conv_name][index_i][index_j] = \
oristate_dict[conv_name][i][j]
elif name=='pre_layers':
cnt += 1
cov_id = cnt
# print(f'pre_layers: {cov_id}')
honey_filter_index = ['.0'] # the index of sketch filter weight
honey_bn_index = ['.1'] # the index of sketch bn weight
for bn_index in honey_bn_index:
all_honey_bn_name.append(name + bn_index)
for weight_index in honey_filter_index:
conv_name = name + weight_index + '.weight'
all_honey_conv_name.append(name + weight_index)
oriweight = oristate_dict[conv_name]
curweight =state_dict[args.name_base+conv_name]
orifilter_num = oriweight.size(0)
currentfilter_num = curweight.size(0)
if orifilter_num != currentfilter_num:
# print(f'{prefix + str(cov_id) + subfix}')
rank = np.load(prefix + str(cov_id) + subfix)
select_index = np.argsort(rank)[orifilter_num - currentfilter_num:] # preserved filter id
select_index.sort()
cur_last_select_index = select_index[:]
for index_i, i in enumerate(select_index):
state_dict[args.name_base+conv_name][index_i] = \
oristate_dict[conv_name][i]#'''
for name, module in model.named_modules(): # Reassign non sketch weights to the new network
name = name.replace('module.', '')
if isinstance(module, nn.Conv2d):
if name not in all_honey_conv_name:
state_dict[args.name_base+name + '.weight'] = oristate_dict[name + '.weight']
state_dict[args.name_base+name + '.bias'] = oristate_dict[name + '.bias']
elif isinstance(module, nn.BatchNorm2d):
if name not in all_honey_bn_name:
state_dict[args.name_base+name + '.weight'] = oristate_dict[name + '.weight']
state_dict[args.name_base+name + '.bias'] = oristate_dict[name + '.bias']
state_dict[args.name_base+name + '.running_mean'] = oristate_dict[name + '.running_mean']
state_dict[args.name_base+name + '.running_var'] = oristate_dict[name + '.running_var']
elif isinstance(module, nn.Linear):
state_dict[args.name_base+name + '.weight'] = oristate_dict[name + '.weight']
state_dict[args.name_base+name + '.bias'] = oristate_dict[name + '.bias']
model.load_state_dict(state_dict)
# def load_google_model(args, logger, model, oristate_dict):
# state_dict = model.state_dict()
#
# filters = [
# [64, 128, 32, 32],
# [128, 192, 96, 64],
# [192, 208, 48, 64],
# [160, 224, 64, 64],
# [128, 256, 64, 64],
# [112, 288, 64, 64],
# [256, 320, 128, 128],
# [256, 320, 128, 128],
# [384, 384, 128, 128]
# ]
#
# #last_select_index = []
# all_honey_conv_name = []
# all_honey_bn_name = []
# cur_last_select_index = []
#
# cnt=0
# prefix = args.rank_conv_prefix+'/rank_conv_w'
# subfix = ".npy"
# for name, module in model.named_modules():
# name = name.replace('module.', '')
#
# if isinstance(module, Inception):
#
# cnt += 1
# cov_id = cnt
#
# honey_filter_channel_index = [
# '.branch5x5.6',
# ] # the index of sketch filter and channel weight
# honey_channel_index = [
# '.branch1x1.0',
# '.branch3x3.0',
# '.branch5x5.0',
# '.branch_pool.1'
# ] # the index of sketch channel weight
# honey_filter_index = [
# '.branch3x3.3',
# '.branch5x5.3',
# ] # the index of sketch filter weight
# honey_bn_index = [
# '.branch3x3.4',
# '.branch5x5.4',
# '.branch5x5.7',
# ] # the index of sketch bn weight
#
# for bn_index in honey_bn_index:
# all_honey_bn_name.append(name + bn_index)
#
# last_select_index = cur_last_select_index[:]
# cur_last_select_index=[]
#
# for weight_index in honey_channel_index:
#
# if '3x3' in weight_index:
# branch_name='_n3x3'
# elif '5x5' in weight_index:
# branch_name='_n5x5'
# elif '1x1' in weight_index:
# branch_name='_n1x1'
# elif 'pool' in weight_index:
# branch_name='_pool_planes'
#
# conv_name = name + weight_index + '.weight'
# all_honey_conv_name.append(name + weight_index)
#
# oriweight = oristate_dict[conv_name]
# curweight =state_dict[args.name_base+conv_name]
# orifilter_num = oriweight.size(1)
# currentfilter_num = curweight.size(1)
#
# if orifilter_num != currentfilter_num:
# select_index = last_select_index
# else:
# select_index = list(range(0, orifilter_num))
#
# for i in range(state_dict[args.name_base+conv_name].size(0)):
# for index_j, j in enumerate(select_index):
# state_dict[args.name_base+conv_name][i][index_j] = \
# oristate_dict[conv_name][i][j]
#
# if branch_name=='_n1x1':
# tmp_select_index = list(range(state_dict[args.name_base+conv_name].size(0)))
# cur_last_select_index += tmp_select_index
# if branch_name=='_pool_planes':
# tmp_select_index = list(range(state_dict[args.name_base+conv_name].size(0)))
# tmp_select_index = [x+filters[cov_id-2][0]+filters[cov_id-2][1]+filters[cov_id-2][2] for x in tmp_select_index]
# cur_last_select_index += tmp_select_index
#
# for weight_index in honey_filter_index:
#
# if '3x3' in weight_index:
# branch_name='_n3x3'
# elif '5x5' in weight_index:
# branch_name='_n5x5'
# elif '1x1' in weight_index:
# branch_name='_n1x1'
# elif 'pool' in weight_index:
# branch_name='_pool_planes'
#
# conv_name = name + weight_index + '.weight'
#
# all_honey_conv_name.append(name + weight_index)
# oriweight = oristate_dict[conv_name]
# curweight =state_dict[args.name_base+conv_name]
#
# orifilter_num = oriweight.size(0)
# currentfilter_num = curweight.size(0)
#
# if orifilter_num != currentfilter_num:
# logger.info('loading rank from: ' + prefix + str(cov_id) + branch_name + subfix)
# rank = np.load(prefix + str(cov_id) + branch_name + subfix)
# select_index = np.argsort(rank)[orifilter_num - currentfilter_num:] # preserved filter id
# select_index.sort()
# else:
# select_index = list(range(0, orifilter_num))
#
# for index_i, i in enumerate(select_index):
# state_dict[args.name_base+conv_name][index_i] = \
# oristate_dict[conv_name][i]
#
# if branch_name=='_n3x3':
# tmp_select_index = [x+filters[cov_id-2][0] for x in select_index]
# cur_last_select_index += tmp_select_index
# if branch_name=='_n5x5':
# last_select_index=select_index
#
# for weight_index in honey_filter_channel_index:
#
# if '3x3' in weight_index:
# branch_name='_n3x3'
# elif '5x5' in weight_index:
# branch_name='_n5x5'
# elif '1x1' in weight_index:
# branch_name='_n1x1'
# elif 'pool' in weight_index:
# branch_name='_pool_planes'
#
# conv_name = name + weight_index + '.weight'
# all_honey_conv_name.append(name + weight_index)
#
# oriweight = oristate_dict[conv_name]
# curweight = state_dict[args.name_base+conv_name]
#
# orifilter_num = oriweight.size(1)
# currentfilter_num = curweight.size(1)
#
# if orifilter_num != currentfilter_num:
# select_index = last_select_index
# else:
# select_index = range(0, orifilter_num)
#
# orifilter_num = oriweight.size(0)
# currentfilter_num = curweight.size(0)
#
# select_index_1 = copy.deepcopy(select_index)
#
# if orifilter_num != currentfilter_num:
# logger.info('loading rank from: ' + prefix + str(cov_id) + branch_name + subfix)
# rank = np.load(prefix + str(cov_id) + branch_name + subfix)
# select_index = np.argsort(rank)[orifilter_num - currentfilter_num:] # preserved filter id
# select_index.sort()
#
# else:
# select_index = list(range(0, orifilter_num))
#
# if branch_name == '_n5x5':
# tmp_select_index = [x+filters[cov_id-2][0]+filters[cov_id-2][1] for x in select_index]
# cur_last_select_index += tmp_select_index
#
# for index_i, i in enumerate(select_index):
# for index_j, j in enumerate(select_index_1):
# state_dict[args.name_base+conv_name][index_i][index_j] = \
# oristate_dict[conv_name][i][j]
#
# elif name=='pre_layers':
#
# cnt += 1
# cov_id = cnt
#
# honey_filter_index = ['.0'] # the index of sketch filter weight
# honey_bn_index = ['.1'] # the index of sketch bn weight
#
# for bn_index in honey_bn_index:
# all_honey_bn_name.append(name + bn_index)
#
# for weight_index in honey_filter_index:
#
# conv_name = name + weight_index + '.weight'
#
# all_honey_conv_name.append(name + weight_index)
# oriweight = oristate_dict[conv_name]
# curweight =state_dict[args.name_base+conv_name]
#
# orifilter_num = oriweight.size(0)
# currentfilter_num = curweight.size(0)
#
# if orifilter_num != currentfilter_num:
# rank = np.load(prefix + str(cov_id) + subfix)
# select_index = np.argsort(rank)[orifilter_num - currentfilter_num:] # preserved filter id
# select_index.sort()
#
# cur_last_select_index = select_index[:]
#
# for index_i, i in enumerate(select_index):
# state_dict[args.name_base+conv_name][index_i] = \
# oristate_dict[conv_name][i]#'''
#
# for name, module in model.named_modules(): # Reassign non sketch weights to the new network
# name = name.replace('module.', '')
#
# if isinstance(module, nn.Conv2d):
# if name not in all_honey_conv_name:
# state_dict[args.name_base+name + '.weight'] = oristate_dict[name + '.weight']
# state_dict[args.name_base+name + '.bias'] = oristate_dict[name + '.bias']
#
# elif isinstance(module, nn.BatchNorm2d):
#
# if name not in all_honey_bn_name:
# state_dict[args.name_base+name + '.weight'] = oristate_dict[name + '.weight']
# state_dict[args.name_base+name + '.bias'] = oristate_dict[name + '.bias']
# state_dict[args.name_base+name + '.running_mean'] = oristate_dict[name + '.running_mean']
# state_dict[args.name_base+name + '.running_var'] = oristate_dict[name + '.running_var']
#
# elif isinstance(module, nn.Linear):
# state_dict[args.name_base+name + '.weight'] = oristate_dict[name + '.weight']
# state_dict[args.name_base+name + '.bias'] = oristate_dict[name + '.bias']
#
# model.load_state_dict(state_dict)
def load_densenet_model(args, logger, model, oristate_dict):
state_dict = model.state_dict()
last_select_index = [] #Conv index selected in the previous layer
cnt=0
prefix = args.rank_conv_prefix+'/rank_conv_w'
subfix = ".npy"
for name, module in model.named_modules():
name = name.replace('module.', '')
if isinstance(module, nn.Conv2d):
cnt+=1
cov_id = cnt
oriweight = oristate_dict[name + '.weight']
curweight = state_dict[args.name_base+name + '.weight']
orifilter_num = oriweight.size(0)
currentfilter_num = curweight.size(0)
if orifilter_num != currentfilter_num:
logger.info('loading rank from: ' + prefix + str(cov_id) + subfix)
rank = np.load(prefix + str(cov_id) + subfix)
select_index = list(np.argsort(rank)[orifilter_num-currentfilter_num:]) # preserved filter id
select_index.sort()
if last_select_index is not None:
for index_i, i in enumerate(select_index):
for index_j, j in enumerate(last_select_index):
state_dict[args.name_base+name + '.weight'][index_i][index_j] = \
oristate_dict[name + '.weight'][i][j]
else:
for index_i, i in enumerate(select_index):
state_dict[args.name_base+name + '.weight'][index_i] = \
oristate_dict[name + '.weight'][i]
elif last_select_index is not None:
for i in range(orifilter_num):
for index_j, j in enumerate(last_select_index):
state_dict[args.name_base+name + '.weight'][i][index_j] = \
oristate_dict[name + '.weight'][i][j]
select_index = list(range(0, orifilter_num))
else:
select_index = list(range(0, orifilter_num))
state_dict[args.name_base+name + '.weight'] = oriweight
if cov_id==1 or cov_id==14 or cov_id==27:
last_select_index = select_index
else:
tmp_select_index = [x+cov_id*12-(cov_id-1)//13*12 for x in select_index]
last_select_index += tmp_select_index
model.load_state_dict(state_dict)
| 42.506747
| 133
| 0.535377
| 3,232
| 28,352
| 4.393874
| 0.054455
| 0.103796
| 0.051264
| 0.067038
| 0.949933
| 0.9393
| 0.927188
| 0.921344
| 0.903387
| 0.89705
| 0
| 0.02616
| 0.362267
| 28,352
| 667
| 134
| 42.506747
| 0.75925
| 0.373554
| 0
| 0.703593
| 0
| 0
| 0.047638
| 0
| 0
| 0
| 0
| 0
| 0.005988
| 1
| 0.011976
| false
| 0
| 0.011976
| 0
| 0.023952
| 0.002994
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
24439b8b5a08a8fea4d90dba492b2bf785475372
| 32
|
py
|
Python
|
practice/2.py
|
porala/python
|
41213189a9b35b5b8c40c048f4d6cd3f8e5f25f4
|
[
"DOC"
] | 1
|
2020-01-15T11:04:16.000Z
|
2020-01-15T11:04:16.000Z
|
practice/2.py
|
porala/python
|
41213189a9b35b5b8c40c048f4d6cd3f8e5f25f4
|
[
"DOC"
] | 2
|
2021-03-31T19:36:19.000Z
|
2021-06-10T22:29:26.000Z
|
practice/2.py
|
porala/python
|
41213189a9b35b5b8c40c048f4d6cd3f8e5f25f4
|
[
"DOC"
] | null | null | null |
a = 1
_a = 2
_a2 = 3
2a = 4
| 6.4
| 8
| 0.375
| 8
| 32
| 1.25
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.375
| 0.5
| 32
| 4
| 9
| 8
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
246bc16870ffd56840f75a780cee54c6657f4abb
| 2,273
|
py
|
Python
|
home/migrations/0011_partner_sayhello_sponsor.py
|
witty-technologies-empowerment/codeupblood
|
a0aa1725e5776d80e083b6d4e9e67476bb97e983
|
[
"MIT"
] | null | null | null |
home/migrations/0011_partner_sayhello_sponsor.py
|
witty-technologies-empowerment/codeupblood
|
a0aa1725e5776d80e083b6d4e9e67476bb97e983
|
[
"MIT"
] | null | null | null |
home/migrations/0011_partner_sayhello_sponsor.py
|
witty-technologies-empowerment/codeupblood
|
a0aa1725e5776d80e083b6d4e9e67476bb97e983
|
[
"MIT"
] | 1
|
2022-01-19T11:09:13.000Z
|
2022-01-19T11:09:13.000Z
|
# Generated by Django 3.1.6 on 2021-03-30 10:14
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('home', '0010_auto_20210318_0009'),
]
operations = [
migrations.CreateModel(
name='Partner',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('picture', models.ImageField(upload_to='home/partner')),
('name', models.CharField(max_length=150)),
('type_of_sponsor', models.CharField(max_length=150)),
('as_person', models.BooleanField(default=False)),
('as_buiness', models.BooleanField(default=False)),
('created', models.DateTimeField(auto_now_add=True)),
],
options={
'ordering': ['-created'],
},
),
migrations.CreateModel(
name='SayHello',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=150)),
('email', models.CharField(max_length=250)),
('subject', models.CharField(max_length=750)),
('message', models.CharField(max_length=10000)),
('created', models.DateTimeField(auto_now_add=True)),
],
options={
'ordering': ['-created'],
},
),
migrations.CreateModel(
name='Sponsor',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('picture', models.ImageField(upload_to='home/sponsor')),
('name', models.CharField(max_length=150)),
('type_of_sponsor', models.CharField(max_length=150)),
('as_person', models.BooleanField(default=False)),
('as_buiness', models.BooleanField(default=False)),
('created', models.DateTimeField(auto_now_add=True)),
],
options={
'ordering': ['-created'],
},
),
]
| 39.189655
| 114
| 0.534976
| 206
| 2,273
| 5.728155
| 0.325243
| 0.101695
| 0.122034
| 0.162712
| 0.737288
| 0.737288
| 0.711017
| 0.711017
| 0.711017
| 0.711017
| 0
| 0.036846
| 0.319402
| 2,273
| 57
| 115
| 39.877193
| 0.725921
| 0.019798
| 0
| 0.647059
| 1
| 0
| 0.119946
| 0.010332
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.019608
| 0
| 0.078431
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
0328311456ddac0328d91c80d87301561431b48f
| 3,612
|
py
|
Python
|
test/test_violations_per_file.py
|
rschuitema/misra
|
e451f7b08f41bc2e0234897bfcce84a71d2c2af9
|
[
"MIT"
] | null | null | null |
test/test_violations_per_file.py
|
rschuitema/misra
|
e451f7b08f41bc2e0234897bfcce84a71d2c2af9
|
[
"MIT"
] | 1
|
2018-04-02T07:46:26.000Z
|
2018-04-02T07:46:26.000Z
|
test/test_violations_per_file.py
|
rschuitema/misra
|
e451f7b08f41bc2e0234897bfcce84a71d2c2af9
|
[
"MIT"
] | null | null | null |
from src.misra.misra_guideline import MisraGuideline
from src.queries.violations_per_file import get_violations_per_file
def test_violations_per_file_success():
guidelines = {"1.1": MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")),
"1.5": MisraGuideline(("1.5", "rule", "Mandatory", "Parameters", "Functions rule 2")),
"1.7": MisraGuideline(("1.7", "rule", "Mandatory", "Functions", "Functions rule 3")),
"2.1": MisraGuideline(("2.1", "rule", "Mandatory", "Layout", "Functions rule 4")),
"2.2": MisraGuideline(("2.2", "rule", "Mandatory", "Layout", "Functions rule 5"))}
guideline_violations = [
(MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")), "test.c", "12", "34", "var"),
(MisraGuideline(("2.1", "rule", "Mandatory", "Layout", "Functions rule 1")), "test.c", "12", "34", "var"),
(MisraGuideline(("2.1", "rule", "Mandatory", "Layout", "Functions rule 1")), "welcome.c", "12", "34", "var"),
(MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")), "welcome.c", "12", "34", "var"),
(MisraGuideline(("1.7", "rule", "Mandatory", "Functions", "Functions rule 1")), "welcome.c", "12", "34", "var"),
(MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")), "hello.c", "12", "34", "var"),
(MisraGuideline(("1.5", "rule", "Mandatory", "Parameters", "Functions rule 1")), "test.c", "12", "34", "var"),
]
violations_per_file = get_violations_per_file(guideline_violations, guidelines)
assert 3 == len(violations_per_file)
assert 1 == violations_per_file['hello.c']
assert 3 == violations_per_file['welcome.c']
assert 3 == violations_per_file['test.c']
def test_no_violations_empty_violations_per_file():
guidelines = {"1.1": MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")),
"1.5": MisraGuideline(("1.5", "rule", "Mandatory", "Parameters", "Functions rule 2")),
"1.7": MisraGuideline(("1.7", "rule", "Mandatory", "Functions", "Functions rule 3")),
"2.1": MisraGuideline(("2.1", "rule", "Mandatory", "Layout", "Functions rule 4")),
"2.2": MisraGuideline(("2.2", "rule", "Mandatory", "Layout", "Functions rule 5"))}
guideline_violations = []
violations_per_file = get_violations_per_file(guideline_violations, guidelines)
assert 0 == len(violations_per_file)
def test_no_guidelines_empty_violations_per_file():
guidelines = {}
guideline_violations = [
(MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")), "test.c", "12", "34", "var"),
(MisraGuideline(("2.1", "rule", "Mandatory", "Layout", "Functions rule 1")), "test.c", "12", "34", "var"),
(MisraGuideline(("2.1", "rule", "Mandatory", "Layout", "Functions rule 1")), "welcome.c", "12", "34", "var"),
(MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")), "welcome.c", "12", "34", "var"),
(MisraGuideline(("1.7", "rule", "Mandatory", "Functions", "Functions rule 1")), "welcome.c", "12", "34", "var"),
(MisraGuideline(("1.1", "rule", "Mandatory", "Functions", "Functions rule 1")), "hello.c", "12", "34", "var"),
(MisraGuideline(("1.5", "rule", "Mandatory", "Parameters", "Functions rule 1")), "test.c", "12", "34", "var"),
]
violations_per_file = get_violations_per_file(guideline_violations, guidelines)
assert 0 == len(violations_per_file)
| 57.333333
| 120
| 0.599114
| 424
| 3,612
| 4.974057
| 0.087264
| 0.147937
| 0.137032
| 0.053106
| 0.903746
| 0.863442
| 0.839734
| 0.839734
| 0.839734
| 0.839734
| 0
| 0.051887
| 0.178295
| 3,612
| 62
| 121
| 58.258065
| 0.658693
| 0
| 0
| 0.704545
| 0
| 0
| 0.337209
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 1
| 0.068182
| false
| 0
| 0.045455
| 0
| 0.113636
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
034376cf96579535c722f26ead779b87f711b53c
| 220
|
py
|
Python
|
tarea mt.py
|
carlosdionell/tarea-mt
|
11feaed2ee98fb78d8aec9ec602a80c7c6f7d9b7
|
[
"MIT"
] | null | null | null |
tarea mt.py
|
carlosdionell/tarea-mt
|
11feaed2ee98fb78d8aec9ec602a80c7c6f7d9b7
|
[
"MIT"
] | null | null | null |
tarea mt.py
|
carlosdionell/tarea-mt
|
11feaed2ee98fb78d8aec9ec602a80c7c6f7d9b7
|
[
"MIT"
] | null | null | null |
datos = [0, 100, 0, 0, 0, 0, 0],[0, 0, 0, 200, 0, 800, 0], [25, 30, 0, 0, 0, 0, 0, ], [0, 0, 50, 0, 0, 0, 0, ], [0, 25, 0, 0, 0, 0, 0, ], [0, 0, 0, 0, 50, 0, 40, ], [0, 0, 0, 80, 35, 0, 0, ]
for i in datos:
print(i)
| 55
| 190
| 0.386364
| 56
| 220
| 1.517857
| 0.267857
| 0.658824
| 0.776471
| 0.8
| 0.4
| 0.352941
| 0.352941
| 0.352941
| 0.235294
| 0
| 0
| 0.403846
| 0.290909
| 220
| 3
| 191
| 73.333333
| 0.141026
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 0
| 0
| 1
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
0349d92e1b9a27045409f5f85341a27e4600eb8a
| 229
|
py
|
Python
|
src/python/math_fun/lib/version.py
|
jaximan/pexample
|
8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1
|
[
"Apache-2.0"
] | null | null | null |
src/python/math_fun/lib/version.py
|
jaximan/pexample
|
8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1
|
[
"Apache-2.0"
] | null | null | null |
src/python/math_fun/lib/version.py
|
jaximan/pexample
|
8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1
|
[
"Apache-2.0"
] | null | null | null |
from platform import platform, python_implementation, python_version
import numpy as np
def describe():
return '%s %s with Numpy %s on %s' % (
python_implementation(), python_version(), np.__version__, platform())
| 25.444444
| 78
| 0.716157
| 29
| 229
| 5.37931
| 0.517241
| 0.25641
| 0.333333
| 0.423077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179039
| 229
| 8
| 79
| 28.625
| 0.829787
| 0
| 0
| 0
| 0
| 0
| 0.10917
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.4
| 0.2
| 0.8
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 9
|
037648564fddf5c0314ee699f7388b55af50348e
| 3,321
|
py
|
Python
|
Visualize HRTF.py
|
TheCodeWanderer/Blog-Scripts
|
caeb5fe1118351f7889574d8649c580cfaebdaf8
|
[
"MIT"
] | null | null | null |
Visualize HRTF.py
|
TheCodeWanderer/Blog-Scripts
|
caeb5fe1118351f7889574d8649c580cfaebdaf8
|
[
"MIT"
] | null | null | null |
Visualize HRTF.py
|
TheCodeWanderer/Blog-Scripts
|
caeb5fe1118351f7889574d8649c580cfaebdaf8
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Loads a SOFA file and visualizes the HRTF amplitude and phase
Created on Mon Feb 24 23:08:19 2020
@author: Ivan
"""
#%% Load SOFA file
from SOFASonix import SOFAFile
import numpy as np
import matplotlib.pyplot as plt
import scipy.fft
filename='hrtf_M_hrtf B.sofa'
sofa = SOFAFile.load(filename)
#Get params/data
SR = sofa.Data_SamplingRate
delay = sofa.Data_Delay
pos = sofa.SourcePosition
IR = sofa.Data_IR
N = sofa._N
#%% FFT along equator
ind = pos[:,1]==0 #select where the elevation is zero
pos_pol = pos[ind,0] #only the polar plane (at constant radius and elevation)
IR_pl = IR[ind,:,:] #Filter IR based on the above criteria
ind2 = np.argsort(pos_pol) #sort values to prevent artifcats during plotting
pos_pol = pos_pol[ind2]
IR_pl = IR_pl[ind2,:,:]
xf = scipy.fft.rfftfreq(N,1/SR)
yf = scipy.fft.rfft(IR_pl)
#%% amplitude
plt.pcolormesh(xf,pos_pol,np.abs(yf[:,0,:]),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Left Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Azimuthal angle (deg.)')
plt.xlim([0, 18000])
plt.figure()
plt.pcolormesh(xf,pos_pol,np.abs(yf[:,1,:]),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Right Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Azimuthal angle (deg.)')
plt.xlim([0, 18000])
#%% phase
plt.figure()
plt.pcolormesh(xf,pos_pol,np.arctan2(np.imag(yf[:,0,:]),np.real(yf[:,0,:])),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Left Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Azimuthal angle (deg.)')
plt.xlim([0, 18000])
plt.figure()
plt.pcolormesh(xf,pos_pol,np.arctan2(np.imag(yf[:,1,:]),np.real(yf[:,1,:])),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Right Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Azimuthal angle (deg.)')
plt.xlim([0, 18000])
#%% FFT along polar (xz-plane)
ind = pos[:,0]==0 #select where the azimuth is zero
pos_pol = pos[ind,1] #only the polar plane (at constant radius and elevation)
#IR_pl = IR[ind,:,:] #Filter IR based on the above criteria
IR_pl = IR[ind,:,:] #Filter IR based on the above criteria
ind2 = np.argsort(pos_pol) #sort values to prevent artifcats during plotting
pos_pol = pos_pol[ind2]
IR_pl = IR_pl[ind2,:,:]
xf = scipy.fft.rfftfreq(N,1/SR)
yf = scipy.fft.rfft(IR_pl)
#%% amplitude
plt.figure()
plt.pcolormesh(xf,pos_pol,np.abs(yf[:,0,:]),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Left Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Polar angle (deg.)')
plt.xlim([0, 18000])
plt.figure()
plt.pcolormesh(xf,pos_pol,np.abs(yf[:,1,:]),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Right Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Polar angle (deg.)')
plt.xlim([0, 18000])
#%% phase
plt.figure()
plt.pcolormesh(xf,pos_pol,np.arctan2(np.imag(yf[:,0,:]),np.real(yf[:,0,:])),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Left Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Polar angle (deg.)')
plt.xlim([0, 18000])
plt.figure()
plt.pcolormesh(xf,pos_pol,np.arctan2(np.imag(yf[:,1,:]),np.real(yf[:,1,:])),shading='gouraud',antialiased=True)
plt.colorbar()
plt.title('Right Ear')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Polar angle (deg.)')
plt.xlim([0, 18000])
| 29.651786
| 112
| 0.68112
| 532
| 3,321
| 4.193609
| 0.208647
| 0.04303
| 0.053788
| 0.064545
| 0.808158
| 0.808158
| 0.792022
| 0.792022
| 0.792022
| 0.792022
| 0
| 0.031368
| 0.126468
| 3,321
| 111
| 113
| 29.918919
| 0.737677
| 0.198434
| 0
| 0.817073
| 0
| 0
| 0.16396
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.04878
| 0
| 0.04878
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
063b0abed0207a4e931cc051ec5fc51683b407d1
| 85
|
py
|
Python
|
py.py
|
Biohaxzard/git_Example_meelis_2
|
fd2bf02cf40a8b72a5b435aecdd5905f5a14b928
|
[
"MIT"
] | null | null | null |
py.py
|
Biohaxzard/git_Example_meelis_2
|
fd2bf02cf40a8b72a5b435aecdd5905f5a14b928
|
[
"MIT"
] | null | null | null |
py.py
|
Biohaxzard/git_Example_meelis_2
|
fd2bf02cf40a8b72a5b435aecdd5905f5a14b928
|
[
"MIT"
] | null | null | null |
print("ma olen mjelis maivel")
print("Tere ma olen sus")
print("Tere ma olen su ema")
| 28.333333
| 30
| 0.717647
| 16
| 85
| 3.8125
| 0.5625
| 0.295082
| 0.360656
| 0.491803
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141176
| 85
| 3
| 31
| 28.333333
| 0.835616
| 0
| 0
| 0
| 0
| 0
| 0.651163
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 8
|
065837dac08a9898ab30bb838467a7a6983c0b2d
| 5,546
|
py
|
Python
|
src/imagedataset/operations.py
|
andreamaracani/torch-image-dataset
|
952a60ccc1a13f2f71b017950c56f08cccac7e53
|
[
"Apache-2.0"
] | null | null | null |
src/imagedataset/operations.py
|
andreamaracani/torch-image-dataset
|
952a60ccc1a13f2f71b017950c56f08cccac7e53
|
[
"Apache-2.0"
] | null | null | null |
src/imagedataset/operations.py
|
andreamaracani/torch-image-dataset
|
952a60ccc1a13f2f71b017950c56f08cccac7e53
|
[
"Apache-2.0"
] | null | null | null |
from __future__ import annotations
from typing import Any, Tuple, Optional
import numpy as np
from .enums import Interpolation
class Operation:
""" Abstract class for any operation. """
def __init__(self):
pass
def __call__(self, input: Any) -> Any:
""" Take an input, compute the operation and returns the output. """
raise NotImplementedError(f"{self.__class__.__name__} is an abstract class.")
class ByteLoader(Operation):
""" Class to load bytes. """
def __init__(self):
super().__init__()
def __call__(self, path: str) -> bytes:
""" Take a path, read file and return bytes. """
with open(path, 'rb') as f:
data = f.read()
return data
class FileLoader(Operation):
""" Abstract class for FileLoaders. """
def __init__(self):
super().__init__()
def __call__(self, path: str) -> np.ndarray:
""" Take a path, read file and convert to np.ndarray. """
super().__call__(path)
class FileDecoder(Operation):
""" Abstract class for FileDecoder. """
def __init__(self):
super().__init__()
def __call__(self, bytes: bytes) -> np.ndarray:
""" Take bytes as input and convert to np.ndarray. """
super().__call__(bytes)
class ImageLoader(FileLoader):
""" Abstract class for a FileLoader of images. """
def __init__(self, size: Tuple[int, int], interpolation: Interpolation):
"""
Args:
size (Tuple): the size of image (after resize).
interpolation (Interpolation): the interpolation mode (for resizing).
"""
super().__init__()
self._size = size
self._interpolation = interpolation
def __call__(self, path: str) -> np.ndarray:
""" Take a path, read file and convert to np.ndarray. """
super().__call__(path)
def __repr__(self) -> str:
return f"{self.__class__.__name__}[{self._size}]"
@property
def interpolation(self) -> Interpolation:
""" Get the interpolation of the current ImageLoader. """
return self._interpolation
@property
def size(self) -> Tuple[int, int]:
""" Get the size of the current ImageLoader. """
return self._size
@size.setter
def size(self, size: Tuple[int, int]):
""" Set the size for the current ImageLoader. """
self._size = size
def decoder(self, keep_resizer: Optional[bool] = False) -> ImageDecoder:
"""
Get the corresponding decoder of this ImageLoader.
Args:
keep_resizer (bool, optional): True to keep the resizer also in the
decoder, False to remove the current resizer in the decoder.
"""
raise NotImplementedError(f"{self.__class__.__name__} is an abstract class.")
def resizer(self) -> ImageResizer:
""" Get the corresponding image resizer of this ImageLoader. """
raise NotImplementedError(f"{self.__class__.__name__} is an abstract class.")
class ImageDecoder(FileDecoder):
""" Abstract class for a FileDecoder of images. """
def __init__(self, size: Tuple[int, int], interpolation: Interpolation):
"""
Args:
size (Tuple): the size of image (after resize).
interpolation (Any): the interpolation mode (for resizing).
"""
super().__init__()
self._size = size
self._interpolation = interpolation
@property
def interpolation(self) -> Interpolation:
""" Get the interpolation of the current ImageDecoder. """
return self._interpolation
@property
def size(self) -> Tuple[int, int]:
""" Get the size of the current ImageDecoder. """
return self._size
@size.setter
def size(self, size: Tuple[int, int]):
""" Set the size for the current ImageDecoder. """
self._size = size
def resizer(self) -> ImageResizer:
""" Get the corresponding image resizer of this ImageDecoder. """
raise NotImplementedError(f"{self.__class__.__name__} is an abstract class.")
def __repr__(self) -> str:
return f"{self.__class__.__name__}[{self._size}]"
def __call__(self, bytes: bytes) -> np.ndarray:
""" Take bytes as input and convert to np.ndarray. """
super().__call__(bytes)
class ImageResizer(Operation):
""" Abstract class for an image resizer. """
def __init__(self, size: Tuple[int, int], interpolation: Interpolation):
"""
Args:
size (Tuple): the size of image (after resize).
interpolation (Any): the interpolation mode (for resizing).
"""
super().__init__()
self._size = size
self._interpolation = interpolation
@property
def interpolation(self) -> Interpolation:
""" Get the interpolation of the current ImageDecoder. """
return self._interpolation
@property
def size(self) -> Tuple[int, int]:
""" Get the size of the current ImageDecoder. """
return self._size
@size.setter
def size(self, size: Tuple[int, int]):
""" Set the size for the current ImageDecoder. """
self._size = size
def __repr__(self) -> str:
return f"{self.__class__.__name__}[{self._size}]"
def __call__(self, bytes: np.ndarray) -> np.ndarray:
""" Take a np.ndarray as input and convert to np.ndarray. """
super().__call__(bytes)
| 30.640884
| 85
| 0.608727
| 623
| 5,546
| 5.110754
| 0.136437
| 0.045226
| 0.031093
| 0.030779
| 0.728957
| 0.728957
| 0.715766
| 0.715766
| 0.709485
| 0.709485
| 0
| 0
| 0.275334
| 5,546
| 181
| 86
| 30.640884
| 0.792237
| 0.30797
| 0
| 0.77907
| 0
| 0
| 0.088447
| 0.062518
| 0
| 0
| 0
| 0
| 0
| 1
| 0.337209
| false
| 0.011628
| 0.046512
| 0.034884
| 0.581395
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 8
|
069fd53e8afca169d00f74135e3f041ca1138c38
| 15,197
|
py
|
Python
|
data/typing/numpy.fft.py
|
vfdev-5/python-record-api
|
006faf0bba9cd4cb55fbacc13d2bbda365f5bf0b
|
[
"MIT"
] | null | null | null |
data/typing/numpy.fft.py
|
vfdev-5/python-record-api
|
006faf0bba9cd4cb55fbacc13d2bbda365f5bf0b
|
[
"MIT"
] | null | null | null |
data/typing/numpy.fft.py
|
vfdev-5/python-record-api
|
006faf0bba9cd4cb55fbacc13d2bbda365f5bf0b
|
[
"MIT"
] | null | null | null |
from typing import *
# usage.dask: 1
__name__: object
@overload
def fft(a: pandas.core.series.Series):
"""
usage.pandas: 1
"""
...
@overload
def fft(a: List[float]):
"""
usage.scipy: 3
"""
...
@overload
def fft(a: numpy.ndarray):
"""
usage.dask: 11
usage.matplotlib: 3
usage.scipy: 6
"""
...
@overload
def fft(a: numpy.ndarray, axis: int):
"""
usage.dask: 8
usage.scipy: 6
"""
...
@overload
def fft(a: numpy.ndarray, n: int, axis: int):
"""
usage.dask: 8
usage.matplotlib: 5
usage.scipy: 16
"""
...
@overload
def fft(a: numpy.ndarray, n: int):
"""
usage.dask: 6
usage.scipy: 3
"""
...
@overload
def fft(a: dask.array.core.Array):
"""
usage.dask: 1
"""
...
@overload
def fft(_0: numpy.ndarray, /, *, axes: Tuple[int]):
"""
usage.dask: 3
"""
...
@overload
def fft(a: numpy.ndarray, n: None, axis: int):
"""
usage.dask: 3
"""
...
def fft(
_0: numpy.ndarray = ...,
/,
a: Union[
dask.array.core.Array, numpy.ndarray, pandas.core.series.Series, List[float]
] = ...,
n: Union[None, int] = ...,
axis: int = ...,
*,
axes: Tuple[int] = ...,
):
"""
usage.dask: 40
usage.matplotlib: 8
usage.pandas: 1
usage.scipy: 34
"""
...
@overload
def fft2(a: numpy.ndarray):
"""
usage.dask: 8
"""
...
@overload
def fft2(a: dask.array.core.Array):
"""
usage.dask: 1
"""
...
@overload
def fft2(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def fft2(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def fft2(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
"""
...
@overload
def fft2(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def fft2(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def fft2(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def fft2(
a: Union[numpy.ndarray, dask.array.core.Array],
s: Union[Tuple[int, int], None] = ...,
axes: Tuple[int, ...] = ...,
):
"""
usage.dask: 35
"""
...
@overload
def fftfreq(n: int, d: numpy.ndarray):
"""
usage.skimage: 1
"""
...
@overload
def fftfreq(n: int):
"""
usage.scipy: 7
usage.skimage: 1
"""
...
@overload
def fftfreq(n: int, d: float):
"""
usage.dask: 1
usage.matplotlib: 1
usage.scipy: 22
"""
...
def fftfreq(n: int, d: Union[float, numpy.ndarray] = ...):
"""
usage.dask: 1
usage.matplotlib: 1
usage.scipy: 29
usage.skimage: 2
"""
...
@overload
def fftn(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
usage.scipy: 4
"""
...
@overload
def fftn(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def fftn(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def fftn(a: numpy.ndarray):
"""
usage.dask: 7
"""
...
@overload
def fftn(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def fftn(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def fftn(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def fftn(
a: numpy.ndarray, s: Union[None, Tuple[int, int]] = ..., axes: Tuple[int, ...] = ...
):
"""
usage.dask: 33
usage.scipy: 4
"""
...
@overload
def fftshift(x: numpy.ndarray):
"""
usage.scipy: 1
usage.skimage: 3
"""
...
@overload
def fftshift(x: List[int]):
"""
usage.scipy: 2
"""
...
@overload
def fftshift(x: numpy.ndarray, axes: None):
"""
usage.dask: 1
"""
...
@overload
def fftshift(x: numpy.ndarray, axes: int):
"""
usage.dask: 1
"""
...
@overload
def fftshift(x: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def fftshift(x: numpy.ndarray, axes: Tuple[int, int, int]):
"""
usage.dask: 1
"""
...
def fftshift(
x: Union[numpy.ndarray, List[int]], axes: Union[Tuple[int, ...], int, None] = ...
):
"""
usage.dask: 4
usage.scipy: 3
usage.skimage: 3
"""
...
@overload
def hfft(_0: numpy.ndarray, /, *, axes: Tuple[int]):
"""
usage.dask: 3
"""
...
@overload
def hfft(a: numpy.ndarray):
"""
usage.dask: 10
"""
...
@overload
def hfft(a: numpy.ndarray, n: None, axis: int):
"""
usage.dask: 3
"""
...
@overload
def hfft(a: numpy.ndarray, n: int, axis: int):
"""
usage.dask: 8
"""
...
@overload
def hfft(a: numpy.ndarray, n: int):
"""
usage.dask: 6
"""
...
@overload
def hfft(a: numpy.ndarray, axis: int):
"""
usage.dask: 5
"""
...
def hfft(
_0: numpy.ndarray = ...,
/,
a: numpy.ndarray = ...,
n: Union[int, None] = ...,
axis: int = ...,
*,
axes: Tuple[int] = ...,
):
"""
usage.dask: 35
"""
...
@overload
def ifft(a: List[float]):
"""
usage.scipy: 3
"""
...
@overload
def ifft(a: numpy.ndarray):
"""
usage.dask: 10
usage.scipy: 7
"""
...
@overload
def ifft(a: numpy.ndarray, axis: int):
"""
usage.dask: 6
usage.scipy: 1
"""
...
@overload
def ifft(a: numpy.ndarray, n: int, axis: int):
"""
usage.dask: 8
usage.scipy: 12
"""
...
@overload
def ifft(_0: numpy.ndarray, /, *, axes: Tuple[int]):
"""
usage.dask: 3
"""
...
@overload
def ifft(a: numpy.ndarray, n: None, axis: int):
"""
usage.dask: 3
"""
...
@overload
def ifft(a: numpy.ndarray, n: int):
"""
usage.dask: 6
"""
...
def ifft(
_0: numpy.ndarray = ...,
/,
a: Union[numpy.ndarray, List[float]] = ...,
n: Union[None, int] = ...,
axis: int = ...,
*,
axes: Tuple[int] = ...,
):
"""
usage.dask: 36
usage.scipy: 23
"""
...
@overload
def ifft2(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def ifft2(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def ifft2(a: numpy.ndarray):
"""
usage.dask: 7
"""
...
@overload
def ifft2(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
"""
...
@overload
def ifft2(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def ifft2(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def ifft2(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def ifft2(
a: numpy.ndarray, s: Union[Tuple[int, int], None] = ..., axes: Tuple[int, ...] = ...
):
"""
usage.dask: 33
"""
...
@overload
def ifftn(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def ifftn(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def ifftn(a: numpy.ndarray):
"""
usage.dask: 7
"""
...
@overload
def ifftn(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
"""
...
@overload
def ifftn(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def ifftn(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def ifftn(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def ifftn(
a: numpy.ndarray, s: Union[Tuple[int, int], None] = ..., axes: Tuple[int, ...] = ...
):
"""
usage.dask: 33
"""
...
@overload
def ifftshift(x: List[int]):
"""
usage.scipy: 2
"""
...
@overload
def ifftshift(x: numpy.ndarray):
"""
usage.scipy: 2
"""
...
@overload
def ifftshift(x: numpy.ndarray, axes: None):
"""
usage.dask: 1
"""
...
@overload
def ifftshift(x: numpy.ndarray, axes: int):
"""
usage.dask: 1
"""
...
@overload
def ifftshift(x: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def ifftshift(x: numpy.ndarray, axes: Tuple[int, int, int]):
"""
usage.dask: 1
"""
...
def ifftshift(
x: Union[numpy.ndarray, List[int]], axes: Union[Tuple[int, ...], int, None] = ...
):
"""
usage.dask: 4
usage.scipy: 4
"""
...
@overload
def ihfft(_0: numpy.ndarray, /, *, axes: Tuple[int]):
"""
usage.dask: 3
"""
...
@overload
def ihfft(a: numpy.ndarray):
"""
usage.dask: 10
"""
...
@overload
def ihfft(a: numpy.ndarray, n: None, axis: int):
"""
usage.dask: 3
"""
...
@overload
def ihfft(a: numpy.ndarray, n: int, axis: int):
"""
usage.dask: 8
"""
...
@overload
def ihfft(a: numpy.ndarray, n: int):
"""
usage.dask: 6
"""
...
@overload
def ihfft(a: numpy.ndarray, axis: int):
"""
usage.dask: 5
"""
...
def ihfft(
_0: numpy.ndarray = ...,
/,
a: numpy.ndarray = ...,
n: Union[int, None] = ...,
axis: int = ...,
*,
axes: Tuple[int] = ...,
):
"""
usage.dask: 35
"""
...
@overload
def irfft(a: numpy.ndarray):
"""
usage.dask: 10
usage.scipy: 2
"""
...
@overload
def irfft(a: numpy.ndarray, n: int, axis: int):
"""
usage.dask: 8
usage.scipy: 4
"""
...
@overload
def irfft(_0: numpy.ndarray, /, *, axes: Tuple[int]):
"""
usage.dask: 3
"""
...
@overload
def irfft(a: numpy.ndarray, n: None, axis: int):
"""
usage.dask: 3
"""
...
@overload
def irfft(a: numpy.ndarray, n: int):
"""
usage.dask: 6
"""
...
@overload
def irfft(a: numpy.ndarray, axis: int):
"""
usage.dask: 5
"""
...
def irfft(
_0: numpy.ndarray = ...,
/,
a: numpy.ndarray = ...,
n: Union[None, int] = ...,
axis: int = ...,
*,
axes: Tuple[int] = ...,
):
"""
usage.dask: 35
usage.scipy: 6
"""
...
@overload
def irfft2(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def irfft2(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def irfft2(a: numpy.ndarray):
"""
usage.dask: 7
"""
...
@overload
def irfft2(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
"""
...
@overload
def irfft2(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def irfft2(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def irfft2(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def irfft2(
a: numpy.ndarray, s: Union[Tuple[int, int], None] = ..., axes: Tuple[int, ...] = ...
):
"""
usage.dask: 33
"""
...
@overload
def irfftn(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def irfftn(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def irfftn(a: numpy.ndarray):
"""
usage.dask: 7
"""
...
@overload
def irfftn(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
"""
...
@overload
def irfftn(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def irfftn(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def irfftn(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def irfftn(
a: numpy.ndarray, s: Union[Tuple[int, int], None] = ..., axes: Tuple[int, ...] = ...
):
"""
usage.dask: 33
"""
...
@overload
def rfft(a: pandas.core.series.Series):
"""
usage.pandas: 1
"""
...
@overload
def rfft(a: numpy.ndarray):
"""
usage.dask: 10
usage.scipy: 1
"""
...
@overload
def rfft(a: numpy.ndarray, n: int, axis: int):
"""
usage.dask: 8
usage.scipy: 8
"""
...
@overload
def rfft(_0: numpy.ndarray, /, *, axes: Tuple[int]):
"""
usage.dask: 3
"""
...
@overload
def rfft(a: numpy.ndarray, n: None, axis: int):
"""
usage.dask: 3
"""
...
@overload
def rfft(a: numpy.ndarray, n: int):
"""
usage.dask: 6
"""
...
@overload
def rfft(a: numpy.ndarray, axis: int):
"""
usage.dask: 5
"""
...
def rfft(
_0: numpy.ndarray = ...,
/,
a: Union[numpy.ndarray, pandas.core.series.Series] = ...,
n: Union[None, int] = ...,
axis: int = ...,
*,
axes: Tuple[int] = ...,
):
"""
usage.dask: 35
usage.pandas: 1
usage.scipy: 9
"""
...
@overload
def rfft2(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def rfft2(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def rfft2(a: numpy.ndarray):
"""
usage.dask: 7
"""
...
@overload
def rfft2(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
"""
...
@overload
def rfft2(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def rfft2(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def rfft2(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def rfft2(
a: numpy.ndarray, s: Union[Tuple[int, int], None] = ..., axes: Tuple[int, ...] = ...
):
"""
usage.dask: 33
"""
...
def rfftfreq(n: int, d: float):
"""
usage.dask: 1
usage.scipy: 1
"""
...
@overload
def rfftn(a: numpy.ndarray, axes: Tuple[int, int]):
"""
usage.dask: 9
"""
...
@overload
def rfftn(a: numpy.ndarray, s: None, axes: Tuple[int, int]):
"""
usage.dask: 3
"""
...
@overload
def rfftn(a: numpy.ndarray):
"""
usage.dask: 7
"""
...
@overload
def rfftn(a: numpy.ndarray, s: Tuple[int, int], axes: Tuple[int, int]):
"""
usage.dask: 7
"""
...
@overload
def rfftn(a: numpy.ndarray, s: Tuple[int, int]):
"""
usage.dask: 1
"""
...
@overload
def rfftn(a: numpy.ndarray, axes: Tuple[int]):
"""
usage.dask: 4
"""
...
@overload
def rfftn(a: numpy.ndarray, s: None, axes: Tuple[int]):
"""
usage.dask: 2
"""
...
def rfftn(
a: numpy.ndarray, s: Union[Tuple[int, int], None] = ..., axes: Tuple[int, ...] = ...
):
"""
usage.dask: 33
"""
...
| 13.330702
| 88
| 0.487596
| 1,825
| 15,197
| 4.051507
| 0.038904
| 0.20449
| 0.159048
| 0.073844
| 0.955234
| 0.916554
| 0.876386
| 0.852042
| 0.761428
| 0.710576
| 0
| 0.022141
| 0.298612
| 15,197
| 1,139
| 89
| 13.342406
| 0.671545
| 0.15911
| 0
| 0.683742
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.291759
| false
| 0
| 0.002227
| 0
| 0.293987
| 0
| 0
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| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| 0
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| null | 0
| 0
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| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
06b27e1a5efff02bf823cac347d2671be37a5689
| 175
|
py
|
Python
|
lib/python/treadmill_aws/__init__.py
|
bothejjms/treadmill-aws
|
18d650be09d1054386066fb6993de3af6ec6a42e
|
[
"Apache-2.0"
] | 133
|
2016-09-15T13:36:12.000Z
|
2021-01-18T06:29:13.000Z
|
lib/python/treadmill/__init__.py
|
bretttegart/treadmill
|
812109e31c503a6eddaee2d3f2e1faf2833b6aaf
|
[
"Apache-2.0"
] | 108
|
2016-12-28T23:41:27.000Z
|
2020-03-05T21:20:37.000Z
|
lib/python/treadmill/__init__.py
|
bretttegart/treadmill
|
812109e31c503a6eddaee2d3f2e1faf2833b6aaf
|
[
"Apache-2.0"
] | 69
|
2016-09-23T20:38:58.000Z
|
2020-11-11T02:31:21.000Z
|
"""Treadmill module.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
| 21.875
| 39
| 0.845714
| 21
| 175
| 6.142857
| 0.52381
| 0.310078
| 0.496124
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 175
| 7
| 40
| 25
| 0.832258
| 0.097143
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0.25
| 1
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| 0
| null | 1
| 1
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| 0
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| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 1
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
232c21e8e48fc261acd8b22b58e6147aaa57ab68
| 75
|
py
|
Python
|
appinit_backend/app/lib/users/list.py
|
app-init/backend
|
02bfc059aaa3ba34cb31c2c0cec92391f08826d9
|
[
"MIT"
] | 1
|
2020-09-11T01:20:07.000Z
|
2020-09-11T01:20:07.000Z
|
appinit_backend/app/lib/users/list.py
|
app-init/backend
|
02bfc059aaa3ba34cb31c2c0cec92391f08826d9
|
[
"MIT"
] | null | null | null |
appinit_backend/app/lib/users/list.py
|
app-init/backend
|
02bfc059aaa3ba34cb31c2c0cec92391f08826d9
|
[
"MIT"
] | null | null | null |
from appinit_backend.lib.imports import *
def call(**kwargs):
return []
| 18.75
| 41
| 0.72
| 10
| 75
| 5.3
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146667
| 75
| 4
| 42
| 18.75
| 0.828125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
0005bc504495d68e79826fbad96c719ac8f6ab6b
| 79
|
py
|
Python
|
nonauto/criterions/__init__.py
|
zhengzx-nlp/REDER
|
7035e089e4d30b8090a2c3caa937b1e0ba27cedc
|
[
"MIT"
] | 18
|
2021-11-14T06:34:26.000Z
|
2022-03-19T07:18:08.000Z
|
nonauto/criterions/__init__.py
|
zhengzx-nlp/REDER
|
7035e089e4d30b8090a2c3caa937b1e0ba27cedc
|
[
"MIT"
] | 1
|
2021-12-03T07:23:36.000Z
|
2021-12-10T08:32:36.000Z
|
nonauto/criterions/__init__.py
|
zhengzx-nlp/REDER
|
7035e089e4d30b8090a2c3caa937b1e0ba27cedc
|
[
"MIT"
] | 2
|
2021-12-10T14:20:09.000Z
|
2022-01-08T09:39:27.000Z
|
import nonauto.criterions.nat_loss
import nonauto.criterions.advanced_nmt_loss
| 26.333333
| 43
| 0.898734
| 11
| 79
| 6.181818
| 0.636364
| 0.382353
| 0.676471
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.050633
| 79
| 2
| 44
| 39.5
| 0.906667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
001b90252b509c2b3487c269a069b101b10a399f
| 29
|
py
|
Python
|
tests/basic_test.py
|
Kleijwegt/continuous-integration-with-python
|
3e3c82239b3f73254c2317448701d3b47e9e23dc
|
[
"MIT"
] | null | null | null |
tests/basic_test.py
|
Kleijwegt/continuous-integration-with-python
|
3e3c82239b3f73254c2317448701d3b47e9e23dc
|
[
"MIT"
] | 1
|
2021-09-14T14:32:25.000Z
|
2021-09-14T14:32:25.000Z
|
tests/basic_test.py
|
Kleijwegt/continuous-integration-with-python
|
3e3c82239b3f73254c2317448701d3b47e9e23dc
|
[
"MIT"
] | null | null | null |
assert 1 ==1
print("Passed")
| 9.666667
| 15
| 0.655172
| 5
| 29
| 3.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08
| 0.137931
| 29
| 2
| 16
| 14.5
| 0.68
| 0
| 0
| 0
| 0
| 0
| 0.206897
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0
| true
| 0.5
| 0
| 0
| 0
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
|
0
| 7
|
003f6ab0fe482e0e423f58356c50119c9770ae06
| 9,332
|
py
|
Python
|
yahoo.py
|
iamaris/pystock
|
864f8beba0cf50a7a4f52bf7c67e83fdfd774a9c
|
[
"Apache-2.0"
] | null | null | null |
yahoo.py
|
iamaris/pystock
|
864f8beba0cf50a7a4f52bf7c67e83fdfd774a9c
|
[
"Apache-2.0"
] | null | null | null |
yahoo.py
|
iamaris/pystock
|
864f8beba0cf50a7a4f52bf7c67e83fdfd774a9c
|
[
"Apache-2.0"
] | null | null | null |
# Copyright (c) 2011, Mark Chenoweth
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted
# provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import urllib,time,datetime
class Quote(object):
DATE_FMT = '%Y-%m-%d'
TIME_FMT = '%H:%M:%S'
def __init__(self):
self.symbol = ''
self.date,self.time,self.open_,self.high,self.low,self.close,self.volume = ([] for _ in range(7))
def append(self,dt,open_,high,low,close,volume):
self.date.append(dt.date())
self.time.append(dt.time())
self.open_.append(float(open_))
self.high.append(float(high))
self.low.append(float(low))
self.close.append(float(close))
self.volume.append(int(volume))
def to_csv(self):
return ''.join(["{0},{1},{2},{3:.2f},{4:.2f},{5:.2f},{6:.2f},{7}\n".format(self.symbol,
self.date[bar].strftime('%Y-%m-%d'),self.time[bar].strftime('%H:%M:%S'),
self.open_[bar],self.high[bar],self.low[bar],self.close[bar],self.volume[bar])
for bar in xrange(len(self.close))])
def write_csv(self,filename):
with open(filename,'w') as f:
f.write(self.to_csv())
def read_csv(self,filename):
self.symbol = ''
self.date,self.time,self.open_,self.high,self.low,self.close,self.volume = ([] for _ in range(7))
for line in open(filename,'r'):
symbol,ds,ts,open_,high,low,close,volume = line.rstrip().split(',')
self.symbol = symbol
dt = datetime.datetime.strptime(ds+' '+ts,self.DATE_FMT+' '+self.TIME_FMT)
self.append(dt,open_,high,low,close,volume)
return True
def __repr__(self):
return self.to_csv()
class YahooQuote(Quote):
''' Daily quotes from Yahoo. Date format='yyyy-mm-dd' '''
def __init__(self,symbol,start_date,end_date=datetime.date.today().isoformat()):
super(YahooQuote,self).__init__()
self.symbol = symbol.upper()
start_year,start_month,start_day = start_date.split('-')
start_month = str(int(start_month)-1)
end_year,end_month,end_day = end_date.split('-')
end_month = str(int(end_month)-1)
url_string = "http://ichart.finance.yahoo.com/table.csv?s={0}".format(symbol)
url_string += "&a={0}&b={1}&c={2}".format(start_month,start_day,start_year)
url_string += "&d={0}&e={1}&f={2}".format(end_month,end_day,end_year)
csv = urllib.urlopen(url_string).readlines()
csv.reverse()
for bar in xrange(0,len(csv)-1):
ds,open_,high,low,close,volume,adjc = csv[bar].rstrip().split(',')
open_,high,low,close,adjc = [float(x) for x in [open_,high,low,close,adjc]]
if close != adjc:
factor = adjc/close
open_,high,low,close = [x*factor for x in [open_,high,low,close]]
dt = datetime.datetime.strptime(ds,'%Y-%m-%d')
self.append(dt,open_,high,low,close,volume)
if __name__ == '__main__':
q = YahooQuote('aapl','2011-01-01') # download year to date Apple data
print q # print it out
q = YahooQuote('orcl','2011-02-01','2011-02-28') # download Oracle data for February 2011
q.write_csv('orcl.csv') # save it to disk
q = Quote() # create a generic quote object
q.read_csv('orcl.csv') # populate it with our previously saved data
print q # print it outi
# Copyright (c) 2011, Mark Chenoweth
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted
# provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import urllib,time,datetime
class Quote(object):
DATE_FMT = '%Y-%m-%d'
TIME_FMT = '%H:%M:%S'
def __init__(self):
self.symbol = ''
self.date,self.time,self.open_,self.high,self.low,self.close,self.volume = ([] for _ in range(7))
def append(self,dt,open_,high,low,close,volume):
self.date.append(dt.date())
self.time.append(dt.time())
self.open_.append(float(open_))
self.high.append(float(high))
self.low.append(float(low))
self.close.append(float(close))
self.volume.append(int(volume))
def to_csv(self):
return ''.join(["{0},{1},{2},{3:.2f},{4:.2f},{5:.2f},{6:.2f},{7}\n".format(self.symbol,
self.date[bar].strftime('%Y-%m-%d'),self.time[bar].strftime('%H:%M:%S'),
self.open_[bar],self.high[bar],self.low[bar],self.close[bar],self.volume[bar])
for bar in xrange(len(self.close))])
def write_csv(self,filename):
with open(filename,'w') as f:
f.write(self.to_csv())
def read_csv(self,filename):
self.symbol = ''
self.date,self.time,self.open_,self.high,self.low,self.close,self.volume = ([] for _ in range(7))
for line in open(filename,'r'):
symbol,ds,ts,open_,high,low,close,volume = line.rstrip().split(',')
self.symbol = symbol
dt = datetime.datetime.strptime(ds+' '+ts,self.DATE_FMT+' '+self.TIME_FMT)
self.append(dt,open_,high,low,close,volume)
return True
def __repr__(self):
return self.to_csv()
class YahooQuote(Quote):
''' Daily quotes from Yahoo. Date format='yyyy-mm-dd' '''
def __init__(self,symbol,start_date,end_date=datetime.date.today().isoformat()):
super(YahooQuote,self).__init__()
self.symbol = symbol.upper()
start_year,start_month,start_day = start_date.split('-')
start_month = str(int(start_month)-1)
end_year,end_month,end_day = end_date.split('-')
end_month = str(int(end_month)-1)
url_string = "http://ichart.finance.yahoo.com/table.csv?s={0}".format(symbol)
url_string += "&a={0}&b={1}&c={2}".format(start_month,start_day,start_year)
url_string += "&d={0}&e={1}&f={2}".format(end_month,end_day,end_year)
csv = urllib.urlopen(url_string).readlines()
csv.reverse()
for bar in xrange(0,len(csv)-1):
ds,open_,high,low,close,volume,adjc = csv[bar].rstrip().split(',')
open_,high,low,close,adjc = [float(x) for x in [open_,high,low,close,adjc]]
if close != adjc:
factor = adjc/close
open_,high,low,close = [x*factor for x in [open_,high,low,close]]
dt = datetime.datetime.strptime(ds,'%Y-%m-%d')
self.append(dt,open_,high,low,close,volume)
if __name__ == '__main__':
q = YahooQuote('aapl','2011-01-01') # download year to date Apple data
print q # print it out
q = YahooQuote('orcl','2011-02-01','2011-02-28') # download Oracle data for February 2011
q.write_csv('orcl.csv') # save it to disk
q = Quote() # create a generic quote object
q.read_csv('orcl.csv') # populate it with our previously saved data
print q # print it out::q
| 24.365535
| 128
| 0.646807
| 1,353
| 9,332
| 4.350333
| 0.164819
| 0.024465
| 0.033639
| 0.04893
| 0.99932
| 0.99932
| 0.99932
| 0.998641
| 0.998641
| 0.998641
| 0
| 0.015625
| 0.218174
| 9,332
| 382
| 129
| 24.429319
| 0.791118
| 0.318153
| 0
| 1
| 0
| 0.015873
| 0.078102
| 0.015814
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.015873
| null | null | 0.031746
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
004e0f7d963269c0d405416dd89994e42666a230
| 41
|
py
|
Python
|
chaucha/__init__.py
|
proyecto-chaucha/chaucha-gha
|
efe381029274e3668bed678eb9e8205359be0653
|
[
"MIT"
] | 7
|
2021-02-08T09:51:18.000Z
|
2022-03-27T19:56:56.000Z
|
chaucha/__init__.py
|
proyecto-chaucha/chaucha-gha
|
efe381029274e3668bed678eb9e8205359be0653
|
[
"MIT"
] | 1
|
2022-01-04T22:05:41.000Z
|
2022-01-04T22:05:41.000Z
|
chaucha/__init__.py
|
proyecto-chaucha/chaucha-gha
|
efe381029274e3668bed678eb9e8205359be0653
|
[
"MIT"
] | 4
|
2021-04-13T17:43:19.000Z
|
2022-03-21T23:08:00.000Z
|
from . import crypto
from . import wallet
| 20.5
| 20
| 0.780488
| 6
| 41
| 5.333333
| 0.666667
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170732
| 41
| 2
| 21
| 20.5
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
cc3e9737c349f26bb63797e5599126f70869d84f
| 12,752
|
py
|
Python
|
olc_webportalv2/geneseekr/tests/test_forms.py
|
OLC-Bioinformatics/olc_genomics_portal
|
d70ec669a3a49106f8290fff5dee089726259a23
|
[
"MIT"
] | 3
|
2019-01-03T21:22:21.000Z
|
2019-04-23T15:47:29.000Z
|
olc_webportalv2/geneseekr/tests/test_forms.py
|
OLC-Bioinformatics/olc_genomics_portal
|
d70ec669a3a49106f8290fff5dee089726259a23
|
[
"MIT"
] | 49
|
2019-01-03T18:15:12.000Z
|
2022-03-11T23:37:20.000Z
|
olc_webportalv2/geneseekr/tests/test_forms.py
|
OLC-Bioinformatics/olc_webportalv2
|
d70ec669a3a49106f8290fff5dee089726259a23
|
[
"MIT"
] | 58
|
2019-01-03T21:21:59.000Z
|
2021-11-02T18:00:20.000Z
|
from django.test import TestCase
from django.http import QueryDict
from olc_webportalv2.geneseekr.forms import TreeForm, GeneSeekrForm, AMRForm, ProkkaForm, NearNeighborForm
from olc_webportalv2.metadata.models import SequenceData
from django.core.files.uploadedfile import SimpleUploadedFile
class GeneSeekrFormTest(TestCase):
@classmethod
def setUpTestData(cls):
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0711',
quality='Pass',
genus='Listeria')
sequence_data.save()
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0712',
quality='Pass',
genus='Listeria')
sequence_data.save()
def test_valid_geneseekr_form_seqid_input_fasta_text(self):
form = GeneSeekrForm({
'seqids': '2015-SEQ-0711 2015-SEQ-0712',
'query_sequence': '>fasta_name\nATCGACTGACTAGTCA'
})
self.assertTrue(form.is_valid())
seqid_list, query_sequence = form.cleaned_data
self.assertEqual(seqid_list, ['2015-SEQ-0711', '2015-SEQ-0712'])
self.assertEqual(query_sequence, '>fasta_name\nATCGACTGACTAGTCA')
def test_valid_geneseekr_form_genus_input_fasta_text(self):
form = GeneSeekrForm({
'genus': 'Listeria',
'query_sequence': '>fasta_name\nATCGACTGACTAGTCA'
})
self.assertTrue(form.is_valid())
seqid_list, query_sequence = form.cleaned_data
self.assertEqual(seqid_list, ['2015-SEQ-0711', '2015-SEQ-0712'])
self.assertEqual(query_sequence, '>fasta_name\nATCGACTGACTAGTCA')
def test_valid_geneseekr_form_seqid_input_fasta_file(self):
with open('olc_webportalv2/geneseekr/tests/good_fasta.fasta', 'rb') as upload_file:
form = GeneSeekrForm({'seqids': '2015-SEQ-0711 2015-SEQ-0712'}, {'query_file': SimpleUploadedFile(upload_file.name, upload_file.read())})
self.assertTrue(form.is_valid())
seqid_list, query_sequence = form.cleaned_data
self.assertEqual(seqid_list, ['2015-SEQ-0711', '2015-SEQ-0712'])
def test_valid_geneseekr_form_genus_input_fasta_file(self):
with open('olc_webportalv2/geneseekr/tests/good_fasta.fasta', 'rb') as upload_file:
form = GeneSeekrForm({'genus': 'Listeria'}, {'query_file': SimpleUploadedFile(upload_file.name, upload_file.read())})
self.assertTrue(form.is_valid())
seqid_list, query_sequence = form.cleaned_data
self.assertEqual(seqid_list, ['2015-SEQ-0711', '2015-SEQ-0712'])
def test_invalid_form_missing_seqid(self):
form = GeneSeekrForm({
'seqids': '2222-SEQ-0711 2015-SEQ-0712',
'query_sequence': '>fasta_name\nATCGACTGACTAGTCA'
})
self.assertFalse(form.is_valid())
def test_invalid_form_bad_fasta_file(self):
with open('olc_webportalv2/geneseekr/tests/bad_fasta.fasta', 'rb') as upload_file:
form = GeneSeekrForm({'seqids': '2015-SEQ-0711 2015-SEQ-0712'}, {'query_file': SimpleUploadedFile(upload_file.name, upload_file.read())})
self.assertFalse(form.is_valid())
def test_invalid_form_no_sequences_in_genus(self):
form = GeneSeekrForm({
'genus': 'TotallyFakeGenus',
'query_sequence': '>fasta_name\nATCGACTGACTAGTCA'
})
self.assertFalse(form.is_valid())
def test_invalid_form_no_sequences_in_exclude_genus(self):
form = GeneSeekrForm({
'genus': 'Listeria',
'query_sequence': '>fasta_name\nATCGACTGACTAGTCA',
'everything_but': True,
})
self.assertFalse(form.is_valid())
def test_valid_form_exclude_genus(self):
form = GeneSeekrForm({
'genus': 'Salmonella',
'query_sequence': '>fasta_name\nATCGACTGACTAGTCA',
'everything_but': True,
})
self.assertTrue(form.is_valid())
seqid_list, query_sequence = form.cleaned_data
self.assertEqual(seqid_list, ['2015-SEQ-0711', '2015-SEQ-0712'])
self.assertEqual(query_sequence, '>fasta_name\nATCGACTGACTAGTCA')
def test_invalid_form_fasta_too_long(self):
form = GeneSeekrForm({
'seqids': '2015-SEQ-0711 2015-SEQ-0712',
'query_sequence': '>fasta_name\nATCGACTGACTAGTCA' + 'A'*10000
})
self.assertFalse(form.is_valid())
class TreeFormTest(TestCase):
@classmethod
def setUpTestData(cls):
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0711',
quality='Pass',
genus='Listeria')
sequence_data.save()
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0712',
quality='Pass',
genus='Listeria')
sequence_data.save()
def test_valid_form(self):
form = TreeForm({
'seqids': '2015-SEQ-0711 2015-SEQ-0712'
}, QueryDict())
self.assertTrue(form.is_valid())
seqids, name, number_diversitree_strains, other_files = form.cleaned_data
self.assertEqual(seqids, ['2015-SEQ-0711', '2015-SEQ-0712'])
def test_invalid_form_wrong_seqid_regex(self):
form = TreeForm({
'seqids': '22015-SEQ-0711 2015-SEQ-0712',
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_wrong_seqid_does_not_exist(self):
form = TreeForm({
'seqids': '2222-SEQ-0711 2015-SEQ-0712'
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_negative_number_diversitree_strains(self):
form = TreeForm({
'seqids': '2015-SEQ-0711 2015-SEQ-0712', 'number_diversitree_strains':'-2'
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_large_number_diversitree_strains(self):
form = TreeForm({
'seqids': '2015-SEQ-0711 2015-SEQ-0712', 'number_diversitree_strains':'5'
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_blank(self):
form = TreeForm({
'seqids': ''
}, QueryDict())
self.assertFalse(form.is_valid())
class AMRFormTest(TestCase):
@classmethod
def setUpTestData(cls):
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0711',
quality='Pass',
genus='Listeria')
sequence_data.save()
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0712',
quality='Pass',
genus='Listeria')
sequence_data.save()
def test_valid_form(self):
form = AMRForm({
'seqids': '2015-SEQ-0711 2015-SEQ-0712'
}, QueryDict())
self.assertTrue(form.is_valid())
seqids, name, other_files = form.cleaned_data
self.assertEqual(seqids, ['2015-SEQ-0711', '2015-SEQ-0712'])
def test_invalid_form_wrong_seqid_regex(self):
form = AMRForm({
'seqids': '22015-SEQ-0711 2015-SEQ-0712'
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_wrong_seqid_does_not_exist(self):
form = AMRForm({
'seqids': '2222-SEQ-0711 2015-SEQ-0712'
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_blank(self):
form = AMRForm({
'seqids': ''
}, QueryDict())
self.assertFalse(form.is_valid())
class ProkkaFormTest(TestCase):
@classmethod
def setUpTestData(cls):
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0711',
quality='Pass',
genus='Listeria')
sequence_data.save()
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0712',
quality='Pass',
genus='Listeria')
sequence_data.save()
def test_valid_form(self):
form = ProkkaForm(QueryDict('seqids=2015-SEQ-0711 2015-SEQ-0712'), QueryDict())
self.assertTrue(form.is_valid())
seqids, name, other_files = form.cleaned_data
self.assertEqual(seqids, ['2015-SEQ-0711', '2015-SEQ-0712'])
def test_invalid_form_wrong_seqid_regex(self):
form = ProkkaForm({
'seqids': '22015-SEQ-0711 2015-SEQ-0712'
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_wrong_seqid_does_not_exist(self):
form = ProkkaForm({
'seqids': '2222-SEQ-0711 2015-SEQ-0712'
}, QueryDict())
self.assertFalse(form.is_valid())
def test_invalid_form_blank(self):
form = ProkkaForm({
'seqids': ''
}, QueryDict())
self.assertFalse(form.is_valid())
class NearNeighborsFormTest(TestCase):
@classmethod
def setUpTestData(cls):
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0711',
quality='Pass',
genus='Listeria')
sequence_data.save()
sequence_data = SequenceData.objects.create(seqid='2015-SEQ-0712',
quality='Pass',
genus='Listeria')
sequence_data.save()
def test_valid_form(self):
form = NearNeighborForm({'seqid': '2015-SEQ-0711', 'number_neighbors': 10})
self.assertTrue(form.is_valid())
seqid, name, number_neighbors, uploaded_file = form.cleaned_data
self.assertEqual(seqid, '2015-SEQ-0711')
self.assertEqual(number_neighbors, 10)
def test_negative_neighbors(self):
form = NearNeighborForm({'seqid': '2015-SEQ-0711', 'number_neighbors': -10})
self.assertFalse(form.is_valid())
def test_bad_seqid(self):
form = NearNeighborForm({'seqid': '2015-FAKE-0711', 'number_neighbors': 10})
self.assertFalse(form.is_valid())
def test_neighbor_boundary_low_valid(self):
form = NearNeighborForm({'seqid': '2015-SEQ-0711', 'number_neighbors': 1})
self.assertTrue(form.is_valid())
seqid, name, number_neighbors, uploaded_file = form.cleaned_data
self.assertEqual(seqid, '2015-SEQ-0711')
self.assertEqual(number_neighbors, 1)
def test_neighbor_boundary_high_valid(self):
form = NearNeighborForm({'seqid': '2015-SEQ-0711', 'number_neighbors': 250})
self.assertTrue(form.is_valid())
seqid, name, number_neighbors, uploaded_file = form.cleaned_data
self.assertEqual(seqid, '2015-SEQ-0711')
self.assertEqual(number_neighbors, 250)
def test_neighbor_boundary_high_invalid(self):
form = NearNeighborForm({'seqid': '2015-SEQ-0711', 'number_neighbors': 251})
self.assertFalse(form.is_valid())
def test_neighbor_boundary_low_invalid(self):
form = NearNeighborForm({'seqid': '2015-SEQ-0711', 'number_neighbors': 0})
self.assertFalse(form.is_valid())
def test_neighbor_uploaded_file_no_seqid(self):
with open('olc_webportalv2/geneseekr/tests/good_fasta.fasta', 'rb') as upload_file:
file_data = {'uploaded_file': SimpleUploadedFile(upload_file.name, upload_file.read())}
form = NearNeighborForm({'seqid': '', 'number_neighbors': 4}, file_data)
self.assertTrue(form.is_valid())
def test_neighbor_uploaded_file_bad_extension(self):
with open('olc_webportalv2/test_files/config.xml', 'rb') as upload_file:
form = NearNeighborForm({'seqid': '',
'uploaded_file': SimpleUploadedFile(upload_file.name, upload_file.read()),
'number_neighbors': 4})
self.assertFalse(form.is_valid())
def test_form_invalid_seqid_and_file(self):
with open('olc_webportalv2/geneseekr/tests/good_fasta.fasta', 'rb') as upload_file:
file_data = {'uploaded_file': SimpleUploadedFile(upload_file.name, upload_file.read())}
form = NearNeighborForm({'seqid': '2015-SEQ-0711', 'number_neighbors': 4}, file_data)
self.assertFalse(form.is_valid())
| 43.522184
| 149
| 0.605238
| 1,340
| 12,752
| 5.512687
| 0.090299
| 0.057804
| 0.050629
| 0.045485
| 0.902531
| 0.874509
| 0.856911
| 0.847164
| 0.803032
| 0.778124
| 0
| 0.064184
| 0.275486
| 12,752
| 292
| 150
| 43.671233
| 0.735361
| 0
| 0
| 0.767068
| 0
| 0
| 0.177698
| 0.048463
| 0
| 0
| 0
| 0
| 0.204819
| 1
| 0.156627
| false
| 0.040161
| 0.02008
| 0
| 0.196787
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
aef3957a113e4e40784f01b952947aab351e7af7
| 139
|
py
|
Python
|
narwhallet/core/kcl/addr_book/__init__.py
|
Snider/narwhallet
|
0d528763c735f1e68b8264e302854d41e7cf1956
|
[
"MIT"
] | 3
|
2021-12-29T11:25:13.000Z
|
2022-01-16T13:57:17.000Z
|
narwhallet/core/kcl/addr_book/__init__.py
|
Snider/narwhallet
|
0d528763c735f1e68b8264e302854d41e7cf1956
|
[
"MIT"
] | null | null | null |
narwhallet/core/kcl/addr_book/__init__.py
|
Snider/narwhallet
|
0d528763c735f1e68b8264e302854d41e7cf1956
|
[
"MIT"
] | 1
|
2022-01-16T13:57:20.000Z
|
2022-01-16T13:57:20.000Z
|
from narwhallet.core.kcl.addr_book.book_address import MBookAddress
from narwhallet.core.kcl.addr_book.book_addresses import MBookAddresses
| 69.5
| 71
| 0.892086
| 20
| 139
| 6
| 0.55
| 0.233333
| 0.3
| 0.35
| 0.55
| 0.55
| 0.55
| 0
| 0
| 0
| 0
| 0
| 0.05036
| 139
| 2
| 71
| 69.5
| 0.909091
| 0
| 0
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| 0
| 0
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| 1
| 0
| true
| 0
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| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
4e197365b675df376778fa487438fa952e526562
| 55,400
|
py
|
Python
|
qcrsc/peak.py
|
KevinMMendez/qcrsc
|
15352db3d2414935c02712c72b6b10a6a9628dc5
|
[
"MIT"
] | 1
|
2020-03-30T09:31:14.000Z
|
2020-03-30T09:31:14.000Z
|
qcrsc/peak.py
|
KevinMMendez/qcrsc
|
15352db3d2414935c02712c72b6b10a6a9628dc5
|
[
"MIT"
] | null | null | null |
qcrsc/peak.py
|
KevinMMendez/qcrsc
|
15352db3d2414935c02712c72b6b10a6a9628dc5
|
[
"MIT"
] | null | null | null |
import numpy as np
import pandas as pd
from random import randint
import matplotlib
import matplotlib.pyplot as plt
from bokeh.plotting import ColumnDataSource, figure, output_notebook, show
from bokeh.layouts import gridplot
from bokeh.models.markers import X
from collections import Counter
from bokeh.models.glyphs import Line
from bokeh.models import Label, HoverTool
import warnings
import sys
import os
import sys
from bokeh.io import export_svgs
from .QCRSC import QCRSC
from .control_limits import control_limits
from .table_check import table_check
from .calc_rsd_dratio_blank import calc_rsd_dratio_blank
from .sampletype_binary import sampletype_binary
def peak(DataTable, PeakTable, batch='all', peak='r', gamma='default', transform='log', parametric=True, zero_remove=True, plot='all', control_limit=False, colormap='Accent', fill_points=True, scale_x=1, scale_y=1):
DataTable = sampletype_binary(DataTable) # Create binary columns for ['QC', 'QCT', 'Sample', 'Blank']
if batch == 'all':
batch = list(DataTable.Batch.unique()) # all batches
if type(batch) == int:
batch = [batch] # put batch in a list
if peak == 'r':
peak = PeakTable.Name.sample().values[0] # random peak
if peak == 'R':
peak = PeakTable.Name.sample().values[0] # random peak
if gamma == 'default':
gamma = (0.5, 5, 0.2) # min 0.5, max 5, step 0.2
if plot == 'all':
plot = list(DataTable.SampleType.unique()) # all samples
# Error check: DataTable
table_check(DataTable, print_statement=False)
# Error check: batch
batch_all = list(DataTable.Batch.unique())
for i in batch:
if i not in batch_all:
print("{} is not a batch in the DataTable".format(i))
return
batch_count = Counter(batch)
batch_duplicates = [k for k, v in batch_count.items() if v > 1]
if len(batch_duplicates) > 0:
print("There are duplicates in batch: {}".format(batch_duplicates))
return
# Error check: peak
if peak not in DataTable.columns.values:
print("{} is not a peak in the DataTable".format(peak))
return
# Error check: gamma
if gamma == False:
pass
elif len(gamma) == 3:
pass
else:
print("gamma must be 'default', False, or list/tuple (3 values) e.g. (0.5, 5, 0.2). This is not correct: {}".format(gamma))
return
# Error check: transform
transform_check = ['log', 'glog', False]
if transform not in transform_check:
print("transform must be 'log', 'glog', or False. {} is not an option".format(transform))
return
# Error check: parametric
parametric_check = [True, False]
if parametric not in parametric_check:
print("parametric must be True, or False. {} is not an option".format(parametric))
return
# Error check: zero remove
zero_remove_check = [True, False]
if zero_remove not in zero_remove_check:
print("zero_remove must be True, or False. {} is not an option".format(zero_remove))
return
# Error check: plot
plot_check = list(DataTable.SampleType.unique())
for i in plot:
if i not in plot_check:
print("plot can only include the following: {}. {} is not an option".format(plot_check, i))
return
# Error check: control_limit
if control_limit == False:
pass
elif type(control_limit) == dict:
control_limit_keys = control_limit.keys()
for i in control_limit_keys:
if i not in ['RSD', 'Dratio']:
print("control_limit '{}' is not an option. Possible options are 'RSD', or 'Dratio".format(i))
return
try:
float(control_limit[i])
except ValueError:
print("control_limit dict requires numbers. {} is not a number.".format(control_limit[i]))
return
else:
print("control_limit has to be either False or a dictionary e.g. dict([('RSD', 20)]). {} is not an option".format(control_limit))
return
# Error check scale
if scale_x < 1:
print("Please use a value greater or equal to 1 for scale_x")
return
if scale_y < 1:
print("Please use a value greater or equal to 1 for scale_y")
return
# Error check: fill point
fill_points_check = [True, False]
if fill_points not in fill_points_check:
print("fill_points must be True, or False. {} is not an option".format(zero_remove))
return
# Error check: colormap
colormap_all = "Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r"
colormap_check = [i.strip() for i in colormap_all.split(',')]
if colormap not in colormap_check:
print("{} is not an option for colormap. Possible options include 'Set1', 'Set2', 'rainbow'. All options for colormap can be found here: https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html".format(colormap))
return
# Plot
if len(batch) == 1:
peak_singlebatch(DataTable, PeakTable, batch=batch, peak=peak, gamma=gamma, transform=transform, parametric=parametric, zero_remove=zero_remove, plot=plot, control_limit=control_limit, colormap=colormap, fill_points=fill_points, scale_x=scale_x, scale_y=scale_y)
else:
peak_multibatch(DataTable, PeakTable, batch=batch, peak=peak, gamma=gamma, transform=transform, parametric=parametric, zero_remove=zero_remove, plot=plot, control_limit=control_limit, colormap=colormap, scale_x=scale_x, scale_y=scale_y, fill_points=fill_points)
def peak_singlebatch(DataTable, PeakTable, batch='all', peak='M1', gamma=False, transform='log', parametric=True, zero_remove=True, plot=['Sample', 'QC'], control_limit=False, colormap='Set1', fill_points=True, scale_x=1, scale_y=1):
if gamma != False:
gamma_range = [x / 100.0 for x in range(int(gamma[0] * 100), int(gamma[1] * 100), int(gamma[2] * 100))]
index = PeakTable[PeakTable.Name == peak].index[0] # index of peak
batch_member = np.where(DataTable.Batch == batch[0], 1, 0) # only batch
BatchTable = DataTable[batch_member == 1]
# Extract and transform data
x = BatchTable[peak]
t = BatchTable.Order
qcw = BatchTable.QCW
qcb = BatchTable.QCB
qct = BatchTable.QCT
sam = BatchTable.Sample
blank = BatchTable.Blank
sampletype = BatchTable.SampleType
batch_list = BatchTable.Batch.values
# Check for any QCT (True/False)
qct_check = (qct == 1).any()
qc_check = (qcw == qcb).all()
# Remove zeros and tranform
if zero_remove == True:
x = x.replace(0, np.nan)
if transform is 'log':
x = np.log10(x)
# Calc RSD, D-ratio, and Blank%Mean
Before_RSD_within, Before_Dratio_within, Before_Blank_within = calc_rsd_dratio_blank(x, qcw, sam, blank, transform, parametric)
Before_RSD_between, Before_Dratio_between, Before_Blank_between = calc_rsd_dratio_blank(x, qcb, sam, blank, transform, parametric)
Before_RSD_test, Before_Dratio_test, Before_Blank_test = calc_rsd_dratio_blank(x, qct, sam, blank, transform, parametric)
mpa_mean = np.nanmean(x[qcb == True]) # Need to edit
# perform the QCRSC (if gamma != False)
if gamma != False:
z, f, curvetype, cvMse, gamma_optimal, mpa_median = QCRSC(x, t, qcw, gamma_range)
z = z - mpa_median # not necessary
z = z + mpa_mean # not necessary
After_RSD_within, After_Dratio_within, After_Blank_within = calc_rsd_dratio_blank(z, qcw, sam, blank, transform, parametric)
After_RSD_between, After_Dratio_between, After_Blank_between = calc_rsd_dratio_blank(z, qcb, sam, blank, transform, parametric)
After_RSD_test, After_Dratio_test, After_Blank_test = calc_rsd_dratio_blank(z, qct, sam, blank, transform, parametric)
# Select what to plot (based on plot)
plot_binary = BatchTable['SampleType'].isin(plot)
x = x[plot_binary == True]
t = t[plot_binary == True]
sampletype = sampletype[plot_binary == True]
order = BatchTable.Order[plot_binary == True]
if gamma != False:
z = z[plot_binary == True]
f = np.array(f)
f = f[plot_binary == True]
# Create empty grid (2x2)
grid = np.full((2, 2), None)
# Set width
left_width = 300
right_width = 600
height = 260
# If only scale_x is used
if scale_y == 1:
if scale_x != 1:
right_width = int(right_width * scale_x)
# If only scale_y is used
if scale_y != 1:
if scale_x == 1:
height = int(height * scale_y)
# If both scale_x and scale_y are ysed
if scale_y != 1:
if scale_x != 1:
height = int(height * scale_y)
left_width = int(height * scale_y * 0.75)
right_width = int(right_width * scale_x)
# Set y_label
if transform is 'log':
y_label = 'log(Peak Area)'
else:
y_label = 'Peak Area'
# Get colors
color_sampletype = BatchTable.SampleType[plot_binary == True].values
colmap = plt.get_cmap(colormap)
col = []
for i in range(len(color_sampletype)):
if color_sampletype[i] == 'Blank':
col.append('#00FF00')
elif color_sampletype[i] == 'Sample':
batch_i = batch_list[i]
b_rgb = colmap([batch_i])
b_hex = matplotlib.colors.rgb2hex(b_rgb[0])
col.append(b_hex)
elif color_sampletype[i] == 'QC':
col.append('#FF0000')
elif color_sampletype[i] == 'QCW':
col.append('#FFC000')
elif color_sampletype[i] == 'QCB':
col.append('#FFFC00')
elif color_sampletype[i] == 'QCT':
col.append('#00FFFF')
else:
pass
col = np.array(col)
# Before correction plot
if gamma == False:
before_title = "{}-{}".format(PeakTable.Name[index], PeakTable.Label[index])
else:
before_title = "Before Correction: {}-{}".format(PeakTable.Name[index], PeakTable.Label[index])
grid[0, 1] = figure(title=before_title, plot_width=right_width, plot_height=height, x_axis_label='Order', y_axis_label=y_label)
grid[0, 1].ygrid.visible = False
grid[0, 1].xgrid.visible = False
grid[0, 1].title.text_font_size = '14pt'
# Before: Add control limit
if control_limit != False:
for i in control_limit:
low, upp = control_limits(x, qcb, sam, i, control_limit[i], transform)
before_cl_low = [low] * len(t)
before_cl_upp = [upp] * len(t)
if i == 'RSD':
before_cl_dash = "dashed"
else:
before_cl_dash = "dotdash"
grid[0, 1].line(x=t.values, y=before_cl_low, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
grid[0, 1].line(x=t.values, y=before_cl_upp, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
# Before: Plot line ('X' and dash)
if gamma != False:
grid[0, 1].x(t.values, f, line_width=2, fill_color=None, line_color='black')
grid[0, 1].line(t.values, f, line_width=2, line_dash="dashed", line_color='black')
else:
x_qc_mean_list = np.ones(len(t)) * mpa_mean
grid[0, 1].line(t.values, x_qc_mean_list, line_width=2, line_dash="dashed", line_color='black')
# Before: Plot Samples
if 'Sample' in sampletype.values:
plist = (sampletype == 'Sample').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
if fill_points == True:
glyph_before = grid[0, 1].circle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=8, source=source_before)
else:
glyph_before = grid[0, 1].circle(x="x", y="y", fill_color="white", fill_alpha=0, line_color="color", size=8, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot Blank
if 'Blank' in sampletype.values:
plist = (sampletype == 'Blank').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].diamond(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QC
if 'QC' in sampletype.values:
plist = (sampletype == 'QC').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QCW
if 'QCW' in sampletype.values:
plist = (sampletype == 'QCW').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QCB
if 'QCB' in sampletype.values:
plist = (sampletype == 'QCB').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QCT
if 'QCT' in sampletype.values:
plist = (sampletype == 'QCT').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After and cvMSE plot (if gamma != False)
if gamma != False:
# After correction plot
grid[1, 1] = figure(title="After Correction: {}-{}".format(PeakTable.Name[index], PeakTable.Label[index]), plot_width=right_width, plot_height=height, x_axis_label='Order', y_axis_label=y_label)
grid[1, 1].ygrid.visible = False
grid[1, 1].xgrid.visible = False
grid[1, 1].title.text_font_size = '14pt'
# After: Add control limit
if control_limit != False:
for i in control_limit:
low, upp = control_limits(z, qcb, sam, i, control_limit[i], transform)
before_cl_low = [low] * len(t)
before_cl_upp = [upp] * len(t)
if i == 'RSD':
before_cl_dash = "dashed"
else:
before_cl_dash = "dotdash"
grid[1, 1].line(x=t.values, y=before_cl_low, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
grid[1, 1].line(x=t.values, y=before_cl_upp, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
# After: Plot line (dash)
x_qc_mean_list = np.ones(len(t)) * mpa_mean
grid[1, 1].line(t.values, x_qc_mean_list, line_width=2, line_dash="dashed", line_color='black')
# After: Plot Samples
if 'Sample' in sampletype.values:
plist = (sampletype == 'Sample').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
if fill_points == True:
glyph_after = grid[1, 1].circle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=8, source=source_after)
else:
glyph_after = grid[1, 1].circle(x="x", y="y", fill_color="white", fill_alpha=0, line_color="color", size=8, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot Blank
if 'Blank' in sampletype.values:
plist = (sampletype == 'Blank').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].diamond(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QC
if 'QC' in sampletype.values:
plist = (sampletype == 'QC').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QCW
if 'QCW' in sampletype.values:
plist = (sampletype == 'QCW').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QCB
if 'QCB' in sampletype.values:
plist = (sampletype == 'QCB').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QCT
if 'QCT' in sampletype.values:
plist = (sampletype == 'QCT').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# cvMSE plot
grid[1, 0] = figure(title="Optimisation Curve", plot_width=left_width, plot_height=height, x_axis_label="γ", y_axis_label="cvMSE")
grid[1, 0].ygrid.visible = False
grid[1, 0].xgrid.visible = False
grid[1, 0].title.text_font_size = '14pt'
# cvMSE: Get color
b_rgb = colmap([batch[0]])
b_hex = matplotlib.colors.rgb2hex(b_rgb[0])
# cvMSE: Get optimal
idx_optimal = np.where(np.array(gamma_range) == gamma_optimal)[0]
cvMSE_optimal = cvMse[idx_optimal]
grid[1, 0].circle_x(gamma_optimal, cvMSE_optimal, size=12, line_width=1, line_color='#FF00FF', fill_color='white', alpha=1)
# cvMSE: Plot circles and lines
source_cvMSE = ColumnDataSource(dict(x=gamma_range, y=cvMse))
grid[1, 0].line(x="x", y="y", line_color="grey", line_width=1, source=source_cvMSE)
glyph_cvMSE_circle = grid[1, 0].circle(x="x", y="y", fill_color=b_hex, line_color="grey", fill_alpha=1, size=5, source=source_cvMSE)
# cvMSE: Add HoverTool
grid[1, 0].add_tools(HoverTool(
renderers=[glyph_cvMSE_circle],
tooltips=[
("γ", "@x"),
("cvMSE", "@y"), ],))
# Text box
grid[0, 0] = figure(title="", plot_width=left_width, plot_height=(height + 5), x_axis_label="", y_axis_label="", outline_line_alpha=0)
grid[0, 0].circle(0, 0, line_color='white', fill_color='white', fill_alpha=0) # Necessary to remove warning
grid[0, 0].xaxis.visible = False
grid[0, 0].yaxis.visible = False
grid[0, 0].ygrid.visible = False
grid[0, 0].xgrid.visible = False
# Text box: labels
text_label = []
text_label.append('Batch: {}'.format(batch[0]))
text_label.append('Name: {}'.format(PeakTable.Name[index]))
text_label.append('Label: {}'.format(PeakTable.Label[index]))
if transform is 'log':
text_label.append('log(MPA): {0:.2f}'.format(mpa_mean))
else:
text_label.append('MPA: {0:.2f}'.format(mpa_mean))
if gamma == False:
if qc_check == True:
if qct_check == False:
text_label.append('RSD: {0:.2f}'.format(Before_RSD_within))
text_label.append('D-Ratio: {0:.2f}'.format(Before_Dratio_within))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('RSD Train: {0:.2f}'.format(Before_RSD_within))
text_label.append('RSD Test: {0:.2f}'.format(Before_RSD_test))
text_label.append('D-Ratio Train: {0:.2f}'.format(Before_Dratio_within))
text_label.append('D-Ratio Test: {0:.2f}'.format(Before_Dratio_test))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
if qct_check == False:
text_label.append('RSD Within: {0:.2f}'.format(Before_RSD_within))
text_label.append('RSD Between: {0:.2f}'.format(Before_RSD_between))
text_label.append('D-Ratio Within: {0:.2f}'.format(Before_Dratio_within))
text_label.append('D-Ratio Between: {0:.2f}'.format(Before_Dratio_between))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('RSD Within: {0:.2f}'.format(Before_RSD_within))
text_label.append('RSD Between: {0:.2f}'.format(Before_RSD_between))
text_label.append('RSD Test: {0:.2f}'.format(Before_RSD_test))
text_label.append('D-Ratio Within: {0:.2f}'.format(Before_Dratio_within))
text_label.append('D-Ratio Between: {0:.2f}'.format(Before_Dratio_between))
text_label.append('D-Ratio Test: {0:.2f}'.format(Before_Dratio_test))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
if qc_check == True:
if qct_check == False:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD [BEFORE]AFTER: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('D-Ratio [BEFORE]AFTER: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD Train [BEFORE]AFTER: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('RSD Test [BEFORE]AFTER: [{}] {}'.format(np.round(Before_RSD_test, 2), np.round(After_RSD_test, 2)))
text_label.append('D-Ratio Train [BEFORE]AFTER: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('D-Ratio Test [BEFORE]AFTER: [{}] {}'.format(np.round(Before_Dratio_test, 2), np.round(After_Dratio_test, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
if qct_check == False:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD Within [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('RSD Between [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_between, 2), np.round(After_RSD_between, 2)))
text_label.append('D-Ratio Within [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('D-Ratio Between [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_between, 2), np.round(After_Dratio_between, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD Within [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('RSD Between [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_between, 2), np.round(After_RSD_between, 2)))
text_label.append('RSD Test [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_test, 2), np.round(After_RSD_test, 2)))
text_label.append('D-Ratio Within [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('D-Ratio Between [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_between, 2), np.round(After_Dratio_between, 2)))
text_label.append('D-Ratio Test [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_test, 2), np.round(After_Dratio_test, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
if gamma == False:
if qc_check == True:
if qct_check == False:
text_x = [70] * 7
text_y = [210, 182, 154, 126, 98, 70, 42]
text_size = ['9.1pt'] * 7
else:
text_x = [70] * 9
text_y = [210, 189, 168, 147, 126, 105, 84, 63, 42]
text_size = ['9pt'] * 9
else:
if qct_check == False:
text_x = [70] * 9
text_y = [210, 189, 168, 147, 126, 105, 84, 63, 42]
text_size = ['9pt'] * 9
else:
text_x = [70] * 11
text_y = [210, 193, 176, 159, 142, 125, 108, 91, 74, 57, 40]
text_size = ['8pt'] * 11
else:
if qc_check == True:
if qct_check == False:
text_x = [0] * 9
text_y = [210, 189, 168, 147, 126, 105, 84, 63, 42]
text_size = ['9pt'] * 9
else:
text_x = [0] * 11
text_y = [210, 193, 176, 159, 142, 125, 108, 91, 74, 57, 40]
text_size = ['8pt'] * 11
else:
if qct_check == False:
text_x = [0] * 11
text_y = [210, 193, 176, 159, 142, 125, 108, 91, 74, 57, 40]
text_size = ['8pt'] * 11
else:
text_x = [0] * 13
text_y = [210, 196, 182, 168, 154, 140, 126, 112, 98, 84, 70, 56, 42]
text_size = ['8pt'] * 13
# Add all text
for i in range(len(text_label)):
label = Label(x=text_x[i], y=text_y[i], x_units='screen', y_units='screen', text=text_label[i], text_font_size=text_size[i])
grid[0, 0].add_layout(label)
# Show figure
output_notebook()
fig = gridplot(grid.tolist())
show(fig)
def peak_multibatch(DataTable, PeakTable, batch='all', peak='M1', gamma=False, transform='log', parametric=True, zero_remove=True, plot=['Sample', 'QC'], control_limit=False, colormap='Set1', fill_points=True, scale_x=1, scale_y=1):
if gamma != False:
gamma_range = [x / 100.0 for x in range(int(gamma[0] * 100), int(gamma[1] * 100), int(gamma[2] * 100))]
index = PeakTable[PeakTable.Name == peak].index[0] # index of peak
batch_member = np.where(DataTable.Batch.isin(batch), 1, 0) # only batch
BatchTable = DataTable[batch_member == 1]
# Extract and transform data
x = BatchTable[peak]
t = BatchTable.Order
qcw = BatchTable.QCW
qcb = BatchTable.QCB
qct = BatchTable.QCT
sam = BatchTable.Sample
blank = BatchTable.Blank
sampletype = BatchTable.SampleType
batch_list = BatchTable.Batch.values
# Check for any QCT (True/False)
qct_check = (qct == 1).any()
qc_check = (qcw == qcb).all()
# Remove zeros and tranform
if zero_remove == True:
x = x.replace(0, np.nan)
if transform is 'log':
x = np.log10(x)
# Calc RSD, D-ratio, and Blank%Mean
Before_RSD_within, Before_Dratio_within, Before_Blank_within = calc_rsd_dratio_blank(x, qcw, sam, blank, transform, parametric)
Before_RSD_between, Before_Dratio_between, Before_Blank_between = calc_rsd_dratio_blank(x, qcb, sam, blank, transform, parametric)
Before_RSD_test, Before_Dratio_test, Before_Blank_test = calc_rsd_dratio_blank(x, qct, sam, blank, transform, parametric)
mpa_mean = np.nanmedian(x[qcb == True]) # Need to edit
# perform the QCRSC (if gamma != False)
if gamma != False:
z = []
f = []
curvetype = []
cvMse = []
gamma_optimal = []
mpa_median = []
for i in batch:
batch_i = np.where(batch_list == i, True, False)
x_i = x[batch_i]
t_i = t[batch_i]
qcw_i = qcw[batch_i]
qcb_i = qcb[batch_i]
z_i, f_i, curvetype_i, cvMse_i, gamma_optimal_i, mpa_median_i = QCRSC(x_i, t_i, qcw_i, gamma_range)
mpa_median_i = np.nanmedian(z_i.values[qcb_i == 1])
z_i = z_i - mpa_median_i # align z
z.append(z_i)
f.append(f_i)
curvetype.append(curvetype_i)
cvMse.append(cvMse_i)
gamma_optimal.append(gamma_optimal_i)
mpa_median.append(mpa_median_i)
z = np.array(np.concatenate(z, axis=0))
z = z + np.nanmedian(mpa_median)
f = np.array(np.concatenate(f, axis=0))
mpa_mean = np.nanmedian(mpa_median) # Need to edit
After_RSD_within, After_Dratio_within, After_Blank_within = calc_rsd_dratio_blank(z, qcw, sam, blank, transform, parametric)
After_RSD_between, After_Dratio_between, After_Blank_between = calc_rsd_dratio_blank(z, qcb, sam, blank, transform, parametric)
After_RSD_test, After_Dratio_test, After_Blank_test = calc_rsd_dratio_blank(z, qct, sam, blank, transform, parametric)
# Select what to plot (based on plot)
plot_binary = BatchTable['SampleType'].isin(plot)
x = x[plot_binary == True]
t = t[plot_binary == True]
sampletype = sampletype[plot_binary == True]
order = BatchTable.Order[plot_binary == True]
if gamma != False:
z = z[plot_binary == True]
f = np.array(f)
f = f[plot_binary == True]
# Create empty grid (2x2)
grid = np.full((2, 2), None)
# Set width
left_width = 300
right_width = 600
height = 260
# If only scale_x is used
if scale_y == 1:
if scale_x != 1:
right_width = int(right_width * scale_x)
# If only scale_y is used
if scale_y != 1:
if scale_x == 1:
height = int(height * scale_y)
# If both scale_x and scale_y are ysed
if scale_y != 1:
if scale_x != 1:
height = int(height * scale_y)
left_width = int(height * scale_y * 0.75)
right_width = int(right_width * scale_x)
# Set y_label
if transform is 'log':
y_label = 'log(Peak Area)'
else:
y_label = 'Peak Area'
# Get colors
color_sampletype = BatchTable.SampleType[plot_binary == True].values
colmap = plt.get_cmap(colormap)
col = []
for i in range(len(color_sampletype)):
if color_sampletype[i] == 'Blank':
col.append('#00FF00')
elif color_sampletype[i] == 'Sample':
batch_i = batch_list[i]
b_rgb = colmap([batch_i])
b_hex = matplotlib.colors.rgb2hex(b_rgb[0])
col.append(b_hex)
elif color_sampletype[i] == 'QC':
col.append('#FF0000')
elif color_sampletype[i] == 'QCW':
col.append('#FFC000')
elif color_sampletype[i] == 'QCB':
col.append('#FFFC00')
elif color_sampletype[i] == 'QCT':
col.append('#00FFFF')
else:
pass
col = np.array(col)
# Before correction plot
if gamma == False:
before_title = "{}-{}".format(PeakTable.Name[index], PeakTable.Label[index])
else:
before_title = "Before Correction: {}-{}".format(PeakTable.Name[index], PeakTable.Label[index])
grid[0, 1] = figure(title=before_title, plot_width=right_width, plot_height=height, x_axis_label='Order', y_axis_label=y_label)
grid[0, 1].ygrid.visible = False
grid[0, 1].xgrid.visible = False
grid[0, 1].title.text_font_size = '14pt'
# Before: Add control limit
if control_limit != False:
for i in control_limit:
for j in batch:
batch_j = np.where(batch_list == j, True, False)
x_j = x.values[batch_j]
qcb_j = qcb.values[batch_j]
sam_j = sam.values[batch_j]
t_j = t.values[batch_j]
low, upp = control_limits(x_j, qcb_j, sam_j, i, control_limit[i], transform)
before_cl_low = [low] * len(t_j)
before_cl_upp = [upp] * len(t_j)
if i == 'RSD':
before_cl_dash = "dashed"
else:
before_cl_dash = "dotdash"
grid[0, 1].line(x=t_j, y=before_cl_low, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
grid[0, 1].line(x=t_j, y=before_cl_upp, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
# Before: Plot line ('X' and dash)
if gamma != False:
grid[0, 1].x(t.values, f, line_width=2, fill_color=None, line_color='black')
grid[0, 1].line(t.values, f, line_width=2, line_dash="dashed", line_color='black')
else:
x_qc_mean_list = np.ones(len(t)) * mpa_mean
grid[0, 1].line(t.values, x_qc_mean_list, line_width=2, line_dash="dashed", line_color='black')
# Before: Plot Samples
if 'Sample' in sampletype.values:
plist = (sampletype == 'Sample').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
if fill_points == True:
glyph_before = grid[0, 1].circle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=8, source=source_before)
else:
glyph_before = grid[0, 1].circle(x="x", y="y", fill_color="white", fill_alpha=0, line_color="color", size=8, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot Blank
if 'Blank' in sampletype.values:
plist = (sampletype == 'Blank').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].diamond(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QC
if 'QC' in sampletype.values:
plist = (sampletype == 'QC').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QCW
if 'QCW' in sampletype.values:
plist = (sampletype == 'QCW').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QCB
if 'QCB' in sampletype.values:
plist = (sampletype == 'QCB').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# Before: Plot QCT
if 'QCT' in sampletype.values:
plist = (sampletype == 'QCT').values
source_before = ColumnDataSource(dict(x=t.values[plist], y=x.values[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_before = grid[0, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_before)
grid[0, 1].add_tools(HoverTool(
renderers=[glyph_before],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After and cvMSE plot (if gamma != False)
if gamma != False:
# After correction plot
grid[1, 1] = figure(title="After Correction: {}-{}".format(PeakTable.Name[index], PeakTable.Label[index]), plot_width=right_width, plot_height=height, x_axis_label='Order', y_axis_label=y_label)
grid[1, 1].ygrid.visible = False
grid[1, 1].xgrid.visible = False
grid[1, 1].title.text_font_size = '14pt'
# After: Add control limit
if control_limit != False:
for i in control_limit:
low, upp = control_limits(z, qcb, sam, i, control_limit[i], transform)
before_cl_low = [low] * len(t)
before_cl_upp = [upp] * len(t)
if i == 'RSD':
before_cl_dash = "dashed"
else:
before_cl_dash = "dotdash"
grid[1, 1].line(x=t.values, y=before_cl_low, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
grid[1, 1].line(x=t.values, y=before_cl_upp, line_dash=before_cl_dash, line_width=2, line_color='darkblue')
# After: Plot line (dash)
x_qc_mean_list = np.ones(len(t)) * mpa_mean
grid[1, 1].line(t.values, x_qc_mean_list, line_width=2, line_dash="dashed", line_color='black')
# After: Plot Samples
if 'Sample' in sampletype.values:
plist = (sampletype == 'Sample').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
if fill_points == True:
glyph_after = grid[1, 1].circle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=8, source=source_after)
else:
glyph_after = grid[1, 1].circle(x="x", y="y", fill_color="white", fill_alpha=0, line_color="color", size=8, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot Blank
if 'Blank' in sampletype.values:
plist = (sampletype == 'Blank').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].diamond(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QC
if 'QC' in sampletype.values:
plist = (sampletype == 'QC').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QCW
if 'QCW' in sampletype.values:
plist = (sampletype == 'QCW').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].square(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=9, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QCB
if 'QCB' in sampletype.values:
plist = (sampletype == 'QCB').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# After: Plot QCT
if 'QCT' in sampletype.values:
plist = (sampletype == 'QCT').values
source_after = ColumnDataSource(dict(x=t.values[plist], y=z[plist], label=sampletype[plist], color=col[plist], Name=order[plist]))
glyph_after = grid[1, 1].triangle(x="x", y="y", fill_color="color", fill_alpha=1, line_color='grey', size=12, source=source_after)
grid[1, 1].add_tools(HoverTool(
renderers=[glyph_after],
tooltips=[
("Type", "@label"),
("Order", "@Name"), ],))
# cvMSE plot
grid[1, 0] = figure(title="Optimisation Curve", plot_width=left_width, plot_height=height, x_axis_label="γ", y_axis_label="cvMSE")
grid[1, 0].ygrid.visible = False
grid[1, 0].xgrid.visible = False
grid[1, 0].title.text_font_size = '14pt'
# cvMSE: Per Batch
for i in range(len(cvMse)):
# cvMSE: Get color
b_rgb = colmap([batch[i]])
b_hex = matplotlib.colors.rgb2hex(b_rgb[0])
# cvMSE: Get optimal
idx_optimal = np.where(np.array(gamma_range) == gamma_optimal[i])[0]
cvMSE_optimal = cvMse[i][idx_optimal]
grid[1, 0].circle_x(gamma_optimal[i], cvMSE_optimal, size=12, line_width=1, line_color='#FF00FF', fill_color='white', alpha=1)
# cvMSE: Plot circles and lines
source_cvMSE = ColumnDataSource(dict(x=gamma_range, y=cvMse[i]))
grid[1, 0].line(x="x", y="y", line_color="grey", line_width=1, source=source_cvMSE)
glyph_cvMSE_circle = grid[1, 0].circle(x="x", y="y", fill_color=b_hex, line_color="grey", fill_alpha=0.9, size=5, source=source_cvMSE)
# cvMSE: Add HoverTool
grid[1, 0].add_tools(HoverTool(
renderers=[glyph_cvMSE_circle],
tooltips=[
("γ", "@x"),
("cvMSE", "@y"), ],))
# Text box
grid[0, 0] = figure(title="", plot_width=left_width, plot_height=(height + 5), x_axis_label="", y_axis_label="", outline_line_alpha=0)
grid[0, 0].circle(0, 0, line_color='white', fill_color='white', fill_alpha=0) # Necessary to remove warning
grid[0, 0].xaxis.visible = False
grid[0, 0].yaxis.visible = False
grid[0, 0].ygrid.visible = False
grid[0, 0].xgrid.visible = False
# Text box: labels
text_label = []
text_label.append('Batch: {}'.format(batch[0]))
text_label.append('Name: {}'.format(PeakTable.Name[index]))
text_label.append('Label: {}'.format(PeakTable.Label[index]))
if transform is 'log':
text_label.append('log(MPA): {0:.2f}'.format(mpa_mean))
else:
text_label.append('MPA: {0:.2f}'.format(mpa_mean))
if gamma == False:
if qc_check == True:
if qct_check == False:
text_label.append('RSD: {0:.2f}'.format(Before_RSD_within))
text_label.append('D-Ratio: {0:.2f}'.format(Before_Dratio_within))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('RSD Train: {0:.2f}'.format(Before_RSD_within))
text_label.append('RSD Test: {0:.2f}'.format(Before_RSD_test))
text_label.append('D-Ratio Train: {0:.2f}'.format(Before_Dratio_within))
text_label.append('D-Ratio Test: {0:.2f}'.format(Before_Dratio_test))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
if qct_check == False:
text_label.append('RSD Within: {0:.2f}'.format(Before_RSD_within))
text_label.append('RSD Between: {0:.2f}'.format(Before_RSD_between))
text_label.append('D-Ratio Within: {0:.2f}'.format(Before_Dratio_within))
text_label.append('D-Ratio Between: {0:.2f}'.format(Before_Dratio_between))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('RSD Within: {0:.2f}'.format(Before_RSD_within))
text_label.append('RSD Between: {0:.2f}'.format(Before_RSD_between))
text_label.append('RSD Test: {0:.2f}'.format(Before_RSD_test))
text_label.append('D-Ratio Within: {0:.2f}'.format(Before_Dratio_within))
text_label.append('D-Ratio Between: {0:.2f}'.format(Before_Dratio_between))
text_label.append('D-Ratio Test: {0:.2f}'.format(Before_Dratio_test))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
if qc_check == True:
if qct_check == False:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD [BEFORE]AFTER: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('D-Ratio [BEFORE]AFTER: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD Train [BEFORE]AFTER: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('RSD Test [BEFORE]AFTER: [{}] {}'.format(np.round(Before_RSD_test, 2), np.round(After_RSD_test, 2)))
text_label.append('D-Ratio Train [BEFORE]AFTER: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('D-Ratio Test [BEFORE]AFTER: [{}] {}'.format(np.round(Before_Dratio_test, 2), np.round(After_Dratio_test, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
if qct_check == False:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD Within [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('RSD Between [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_between, 2), np.round(After_RSD_between, 2)))
text_label.append('D-Ratio Within [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('D-Ratio Between [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_between, 2), np.round(After_Dratio_between, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
else:
text_label.append('Correction method: {}'.format(curvetype))
text_label.append('Optimal γ: {}'.format(gamma_optimal))
text_label.append('RSD Within [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_within, 2), np.round(After_RSD_within, 2)))
text_label.append('RSD Between [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_between, 2), np.round(After_RSD_between, 2)))
text_label.append('RSD Test [BEF]AFT: [{}] {}'.format(np.round(Before_RSD_test, 2), np.round(After_RSD_test, 2)))
text_label.append('D-Ratio Within [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_within, 2), np.round(After_Dratio_within, 2)))
text_label.append('D-Ratio Between [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_between, 2), np.round(After_Dratio_between, 2)))
text_label.append('D-Ratio Test [BEF]AFT: [{}] {}'.format(np.round(Before_Dratio_test, 2), np.round(After_Dratio_test, 2)))
text_label.append('Blank%Mean: {0:.2f}'.format(Before_Blank_within))
if gamma == False:
if qc_check == True:
if qct_check == False:
text_x = [70] * 7
text_y = [210, 182, 154, 126, 98, 70, 42]
text_size = ['9.1pt'] * 7
else:
text_x = [70] * 9
text_y = [210, 189, 168, 147, 126, 105, 84, 63, 42]
text_size = ['9pt'] * 9
else:
if qct_check == False:
text_x = [70] * 9
text_y = [210, 189, 168, 147, 126, 105, 84, 63, 42]
text_size = ['9pt'] * 9
else:
text_x = [70] * 11
text_y = [210, 193, 176, 159, 142, 125, 108, 91, 74, 57, 40]
text_size = ['8pt'] * 11
else:
if qc_check == True:
if qct_check == False:
text_x = [0] * 9
text_y = [210, 189, 168, 147, 126, 105, 84, 63, 42]
text_size = ['9pt'] * 9
else:
text_x = [0] * 11
text_y = [210, 193, 176, 159, 142, 125, 108, 91, 74, 57, 40]
text_size = ['8pt'] * 11
else:
if qct_check == False:
text_x = [0] * 11
text_y = [210, 193, 176, 159, 142, 125, 108, 91, 74, 57, 40]
text_size = ['8pt'] * 11
else:
text_x = [0] * 13
text_y = [210, 196, 182, 168, 154, 140, 126, 112, 98, 84, 70, 56, 42]
text_size = ['8pt'] * 13
# Add all text
for i in range(len(text_label)):
label = Label(x=text_x[i], y=text_y[i], x_units='screen', y_units='screen', text=text_label[i], text_font_size=text_size[i])
grid[0, 0].add_layout(label)
# Show figure
output_notebook()
fig = gridplot(grid.tolist())
show(fig)
| 49.90991
| 1,487
| 0.595415
| 7,506
| 55,400
| 4.201172
| 0.056488
| 0.031966
| 0.050422
| 0.022833
| 0.862022
| 0.854633
| 0.853079
| 0.851113
| 0.848196
| 0.845088
| 0
| 0.030293
| 0.258141
| 55,400
| 1,109
| 1,488
| 49.954914
| 0.736977
| 0.043032
| 0
| 0.841395
| 0
| 0.004499
| 0.124839
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.003375
| false
| 0.005624
| 0.023622
| 0
| 0.04387
| 0.017998
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
4e302d3fd9c4b93a4213dc9930146e2006640b0b
| 168
|
py
|
Python
|
fabfile.py
|
openfurther/further-open-config
|
a33d8b5898b5b0146ff23dd964381db1eec48532
|
[
"Apache-2.0"
] | null | null | null |
fabfile.py
|
openfurther/further-open-config
|
a33d8b5898b5b0146ff23dd964381db1eec48532
|
[
"Apache-2.0"
] | null | null | null |
fabfile.py
|
openfurther/further-open-config
|
a33d8b5898b5b0146ff23dd964381db1eec48532
|
[
"Apache-2.0"
] | null | null | null |
# import further-open-fabric-deployment methods so Fabric can see them
from further.deployment import deployFurtherCore
from further.deployment import deployFurtherI2b2
| 56
| 70
| 0.869048
| 21
| 168
| 6.952381
| 0.619048
| 0.150685
| 0.287671
| 0.369863
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013158
| 0.095238
| 168
| 3
| 71
| 56
| 0.947368
| 0.404762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
d1b25de39394ebafa9cadb535b1291d33354d109
| 2,455
|
py
|
Python
|
src/genie/libs/parser/viptela/tests/ShowBfdSessions/cli/equal/golden_output_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 204
|
2018-06-27T00:55:27.000Z
|
2022-03-06T21:12:18.000Z
|
src/genie/libs/parser/viptela/tests/ShowBfdSessions/cli/equal/golden_output_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 468
|
2018-06-19T00:33:18.000Z
|
2022-03-31T23:23:35.000Z
|
src/genie/libs/parser/viptela/tests/ShowBfdSessions/cli/equal/golden_output_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 309
|
2019-01-16T20:21:07.000Z
|
2022-03-30T12:56:41.000Z
|
expected_output = {
"system_ip": {
"172.16.241.1": {
"source_tloc_color": {
"mpls": {
"destination_public_ip": "172.16.171.2",
"destination_public_port": "12346",
"detect_multiplier": "20",
"encapsulation": "ipsec",
"remote_tloc_color": "mpls",
"site_id": "30001001",
"source_ip": "172.16.189.2",
"state": "up",
"transitions": "0",
"tx_interval": "1000",
"uptime": "0:01:46:50",
},
"private1": {
"destination_public_ip": "172.16.171.2",
"destination_public_port": "12346",
"detect_multiplier": "20",
"encapsulation": "ipsec",
"remote_tloc_color": "mpls",
"site_id": "30001001",
"source_ip": "172.16.16.2",
"state": "up",
"transitions": "0",
"tx_interval": "1000",
"uptime": "0:01:46:51",
},
}
},
"172.16.241.2": {
"source_tloc_color": {
"mpls": {
"destination_public_ip": "172.16.34.2",
"destination_public_port": "12346",
"detect_multiplier": "20",
"encapsulation": "ipsec",
"remote_tloc_color": "mpls",
"site_id": "30001002",
"source_ip": "172.16.189.2",
"state": "up",
"transitions": "2",
"tx_interval": "1000",
"uptime": "0:01:41:27",
},
"private1": {
"destination_public_ip": "172.16.34.2",
"destination_public_port": "12346",
"detect_multiplier": "20",
"encapsulation": "ipsec",
"remote_tloc_color": "mpls",
"site_id": "30001002",
"source_ip": "172.16.16.2",
"state": "up",
"transitions": "2",
"tx_interval": "1000",
"uptime": "0:01:41:28",
},
}
},
}
}
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0
| 7
|
ae0ac4039b51cf3f1d19224bd593d99cbfa6140d
| 19,544
|
py
|
Python
|
presets.py
|
TomBesford/LifeGenesis-Legacy
|
4e2e4a24f194d28eb58324a1671f6f7eb01fe0f2
|
[
"Apache-2.0"
] | null | null | null |
presets.py
|
TomBesford/LifeGenesis-Legacy
|
4e2e4a24f194d28eb58324a1671f6f7eb01fe0f2
|
[
"Apache-2.0"
] | null | null | null |
presets.py
|
TomBesford/LifeGenesis-Legacy
|
4e2e4a24f194d28eb58324a1671f6f7eb01fe0f2
|
[
"Apache-2.0"
] | null | null | null |
'''
Contains all preset grids in a dict called PRESETS
'''
PRESETS = {
'Traffic Lights':[
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
],
'Honey Farm':[
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
],
'Oscillators':[
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0],
[0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0],
[0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,0,0,0,0,0,0],
[0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0],
[0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,2,2,0,0,1,0,0,2,2,0,0,0,0,0],
[0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0],
[0,0,0,0,0,0,1,1,1,0,0,0,0,0,2,0,2,0,1,1,0,1,1,0,2,0,2,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,1,0,0,2,2,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0],
[0,0,0,2,2,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0],
[0,0,0,2,2,2,2,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0],
[0,0,0,2,2,2,2,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0],
[0,0,0,2,2,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0],
[0,0,0,0,0,0,2,2,2,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
],
'Pulsar':[
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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}
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0
| 13
|
ae66851eea662c9ad934cf218fae85d9687469bd
| 23,731
|
py
|
Python
|
openapi_client/api/projects_api.py
|
hi-artem/twistlock-py
|
9888e905f5b9d3cc00f9b84244588c0992f8e4f4
|
[
"RSA-MD"
] | null | null | null |
openapi_client/api/projects_api.py
|
hi-artem/twistlock-py
|
9888e905f5b9d3cc00f9b84244588c0992f8e4f4
|
[
"RSA-MD"
] | null | null | null |
openapi_client/api/projects_api.py
|
hi-artem/twistlock-py
|
9888e905f5b9d3cc00f9b84244588c0992f8e4f4
|
[
"RSA-MD"
] | null | null | null |
# coding: utf-8
"""
Prisma Cloud Compute API
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: 21.04.439
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from openapi_client.api_client import ApiClient
from openapi_client.exceptions import ( # noqa: F401
ApiTypeError,
ApiValueError
)
class ProjectsApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def api_v1_projects_get(self, **kwargs): # noqa: E501
"""api_v1_projects_get # noqa: E501
Projects returns the project list that are viewable by the given user # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_get(async_req=True)
>>> result = thread.get()
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: list[TypesProject]
"""
kwargs['_return_http_data_only'] = True
return self.api_v1_projects_get_with_http_info(**kwargs) # noqa: E501
def api_v1_projects_get_with_http_info(self, **kwargs): # noqa: E501
"""api_v1_projects_get # noqa: E501
Projects returns the project list that are viewable by the given user # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(list[TypesProject], status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method api_v1_projects_get" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
response_types_map = {
200: "list[TypesProject]",
}
return self.api_client.call_api(
'/api/v1/projects', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_types_map=response_types_map,
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def api_v1_projects_id_delete(self, id, **kwargs): # noqa: E501
"""api_v1_projects_id_delete # noqa: E501
DeleteProject deletes the specified project # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_id_delete(id, async_req=True)
>>> result = thread.get()
:param id: (required)
:type id: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: TypesProjectCredentials
"""
kwargs['_return_http_data_only'] = True
return self.api_v1_projects_id_delete_with_http_info(id, **kwargs) # noqa: E501
def api_v1_projects_id_delete_with_http_info(self, id, **kwargs): # noqa: E501
"""api_v1_projects_id_delete # noqa: E501
DeleteProject deletes the specified project # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_id_delete_with_http_info(id, async_req=True)
>>> result = thread.get()
:param id: (required)
:type id: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(TypesProjectCredentials, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method api_v1_projects_id_delete" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501
local_var_params['id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `id` when calling `api_v1_projects_id_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in local_var_params:
path_params['id'] = local_var_params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
response_types_map = {
200: "TypesProjectCredentials",
}
return self.api_client.call_api(
'/api/v1/projects/{id}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_types_map=response_types_map,
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def api_v1_projects_id_put(self, id, **kwargs): # noqa: E501
"""api_v1_projects_id_put # noqa: E501
UpdateProject updates the specified project # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_id_put(id, async_req=True)
>>> result = thread.get()
:param id: (required)
:type id: str
:param types_project:
:type types_project: TypesProject
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: TypesProject
"""
kwargs['_return_http_data_only'] = True
return self.api_v1_projects_id_put_with_http_info(id, **kwargs) # noqa: E501
def api_v1_projects_id_put_with_http_info(self, id, **kwargs): # noqa: E501
"""api_v1_projects_id_put # noqa: E501
UpdateProject updates the specified project # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_id_put_with_http_info(id, async_req=True)
>>> result = thread.get()
:param id: (required)
:type id: str
:param types_project:
:type types_project: TypesProject
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(TypesProject, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'id',
'types_project'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method api_v1_projects_id_put" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501
local_var_params['id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `id` when calling `api_v1_projects_id_put`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in local_var_params:
path_params['id'] = local_var_params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'types_project' in local_var_params:
body_params = local_var_params['types_project']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
response_types_map = {
200: "TypesProject",
}
return self.api_client.call_api(
'/api/v1/projects/{id}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_types_map=response_types_map,
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def api_v1_projects_post(self, **kwargs): # noqa: E501
"""api_v1_projects_post # noqa: E501
UpdateProject updates the specified project # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_post(async_req=True)
>>> result = thread.get()
:param types_project:
:type types_project: TypesProject
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: TypesProject
"""
kwargs['_return_http_data_only'] = True
return self.api_v1_projects_post_with_http_info(**kwargs) # noqa: E501
def api_v1_projects_post_with_http_info(self, **kwargs): # noqa: E501
"""api_v1_projects_post # noqa: E501
UpdateProject updates the specified project # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_v1_projects_post_with_http_info(async_req=True)
>>> result = thread.get()
:param types_project:
:type types_project: TypesProject
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(TypesProject, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'types_project'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method api_v1_projects_post" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'types_project' in local_var_params:
body_params = local_var_params['types_project']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
response_types_map = {
200: "TypesProject",
}
return self.api_client.call_api(
'/api/v1/projects', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_types_map=response_types_map,
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
| 41.057093
| 125
| 0.589693
| 2,620
| 23,731
| 5.062214
| 0.077481
| 0.033778
| 0.050667
| 0.032572
| 0.9479
| 0.94496
| 0.943603
| 0.941868
| 0.94119
| 0.940511
| 0
| 0.0149
| 0.341031
| 23,731
| 577
| 126
| 41.12825
| 0.833227
| 0.492225
| 0
| 0.744939
| 1
| 0
| 0.153741
| 0.041549
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036437
| false
| 0
| 0.020243
| 0
| 0.093117
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
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| 0
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| 0
|
0
| 8
|
ae6cd0ef1c078d94f5eed6f4463035aea91b2f47
| 18,798
|
py
|
Python
|
src/utils/casino/table/holdem/HoldemTable_test.py
|
zw-g/Funny-Nation
|
bcb72e802e0ff46b4a409c5d51fc8b10e0987463
|
[
"MIT"
] | null | null | null |
src/utils/casino/table/holdem/HoldemTable_test.py
|
zw-g/Funny-Nation
|
bcb72e802e0ff46b4a409c5d51fc8b10e0987463
|
[
"MIT"
] | null | null | null |
src/utils/casino/table/holdem/HoldemTable_test.py
|
zw-g/Funny-Nation
|
bcb72e802e0ff46b4a409c5d51fc8b10e0987463
|
[
"MIT"
] | null | null | null |
from src.utils.casino.table.holdem.HoldemTable import HoldemTable
from src.utils.poker.Card import Card
def test_Story1():
class MemberTest:
id = 1
owner = MemberTest()
holdemTable = HoldemTable(None, owner)
holdemTable.addPlayer(1)
holdemTable.addPlayer(2)
holdemTable.addPlayer(3)
holdemTable.addPlayer(4)
holdemTable.addPlayer(5)
holdemTable.gameStart()
# Game start
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
# pre-flop (round 1) start
holdemTable.rise(1, 10000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
holdemTable.rise(3, 20000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.callOrCheck(5)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 1
assert holdemTable.mainPot == 152500
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
assert holdemTable.currentBet == 0
assert holdemTable.numberOfPlayersNotFold == 5
# Round 1 end
holdemTable.flop()
# Round 2 start
holdemTable.rise(1, 20000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
holdemTable.fold(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
holdemTable.fold(3)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.rise(5, 10000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
holdemTable.fold(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 4
assert holdemTable.mainPot == 232500
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
assert holdemTable.currentBet == 0
assert holdemTable.numberOfPlayersNotFold == 2
# Round 2 end
holdemTable.turnOrRiver()
# Round 3 start
holdemTable.rise(4, 1000000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.callOrCheck(5)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 4
assert holdemTable.mainPot == 2232500
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
assert holdemTable.currentBet == 0
assert holdemTable.numberOfPlayersNotFold == 2
# Round 3 end
holdemTable.turnOrRiver()
# Round 3 start
holdemTable.rise(4, 10000000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.fold(5)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn is None
assert holdemTable.mainPot == 12232500
assert holdemTable.numberOfPlayersNotFold == 1
# End
def test_story2():
class MemberTest:
id = 1
owner = MemberTest()
holdemTable = HoldemTable(None, owner)
holdemTable.addPlayer(1)
holdemTable.addPlayer(2)
holdemTable.addPlayer(3)
holdemTable.addPlayer(4)
holdemTable.addPlayer(5)
holdemTable.gameStart()
# Game start
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
# pre-flop (round 1) start
holdemTable.rise(1, 10000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
holdemTable.rise(3, 20000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.callOrCheck(5)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 1
assert holdemTable.mainPot == 152500
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
assert holdemTable.currentBet == 0
assert holdemTable.numberOfPlayersNotFold == 5
# Round 1 end
holdemTable.flop()
# Round 2 start
holdemTable.rise(1, 20000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
holdemTable.fold(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
holdemTable.fold(3)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.rise(5, 10000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
holdemTable.fold(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 4
assert holdemTable.mainPot == 232500
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
assert holdemTable.currentBet == 0
assert holdemTable.numberOfPlayersNotFold == 2
# Round 2 end
holdemTable.turnOrRiver()
# Round 3 start
holdemTable.rise(4, 1000000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.callOrCheck(5)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 4
assert holdemTable.mainPot == 2232500
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
assert holdemTable.currentBet == 0
assert holdemTable.numberOfPlayersNotFold == 2
# Round 3 end
holdemTable.turnOrRiver()
# Round 3 start
holdemTable.rise(4, 10000000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.callOrCheck(5)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 4
assert holdemTable.mainPot == 22232500
for playerID in holdemTable.players:
assert holdemTable.players[playerID]['tempPot'] == 0
assert holdemTable.currentBet == 0
assert holdemTable.numberOfPlayersNotFold == 2
# End
def test_Story3():
class MemberTest:
id = 1
owner = MemberTest()
holdemTable = HoldemTable(None, owner)
holdemTable.addPlayer(1)
holdemTable.addPlayer(2)
holdemTable.addPlayer(3)
holdemTable.addPlayer(4)
holdemTable.addPlayer(5)
holdemTable.gameStart()
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante
holdemTable.rise(2, 1000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
assert holdemTable.players[4]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(5)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
assert holdemTable.players[5]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 1
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.flop()
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.rise(2, 10000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 11000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 11000
holdemTable.allIn(4, 100)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
assert holdemTable.players[4]['moneyInvested'] == holdemTable.ante + 1100
holdemTable.allIn(5, 100000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
assert holdemTable.players[5]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.allIn(1, 100)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante + 1100
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 2
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.turnOrRiver()
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 2
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.turnOrRiver()
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.rise(3, 2000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 103000
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 2
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 103000
def test_Story5():
class MemberTest:
id = 1
owner = MemberTest()
holdemTable = HoldemTable(None, owner)
holdemTable.addPlayer(1)
holdemTable.addPlayer(2)
holdemTable.addPlayer(3)
holdemTable.addPlayer(4)
holdemTable.addPlayer(5)
holdemTable.gameStart()
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante
holdemTable.rise(2, 1000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(4)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
assert holdemTable.players[4]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(5)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
assert holdemTable.players[5]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 1
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.flop()
holdemTable.callOrCheck(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.rise(2, 10000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 11000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 11000
holdemTable.allIn(4, 100)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
assert holdemTable.players[4]['moneyInvested'] == holdemTable.ante + 1100
holdemTable.allIn(5, 100000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 1
assert holdemTable.players[5]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.fold(1)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[1]['moneyInvested'] == holdemTable.ante + 1000
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 2
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.turnOrRiver()
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.callOrCheck(3)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 2
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.turnOrRiver()
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 101000
holdemTable.rise(3, 2000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
assert holdemTable.players[3]['moneyInvested'] == holdemTable.ante + 103000
holdemTable.callOrCheck(2)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn == 2
assert holdemTable.players[2]['moneyInvested'] == holdemTable.ante + 103000
holdemTable.board = [
Card(1, 5),
Card(1, 7),
Card(3, 10),
Card(2, 1),
Card(1, 3)
]
holdemTable.players[4]['cards'] = [
Card(1, 4),
Card(1, 6)
]
holdemTable.players[3]['cards'] = [
Card(1, 10),
Card(2, 10)
]
holdemTable.players[2]['cards'] = [
Card(1, 8),
Card(1, 1)
]
holdemTable.players[5]['cards'] = [
Card(1, 6),
Card(3, 6)
]
holdemTable.generateSidePots()
assert len(holdemTable.sidePots) == 4
assert holdemTable.sidePots[0]['money'] == (1000 + holdemTable.ante) * 5
assert len(holdemTable.sidePots[0]['players']) == 4
assert 2 in holdemTable.sidePots[0]['players']
assert 3 in holdemTable.sidePots[0]['players']
assert 4 in holdemTable.sidePots[0]['players']
assert 5 in holdemTable.sidePots[0]['players']
assert holdemTable.sidePots[1]['money'] == (100) * 4
assert len(holdemTable.sidePots[1]['players']) == 4
assert 2 in holdemTable.sidePots[1]['players']
assert 3 in holdemTable.sidePots[1]['players']
assert 4 in holdemTable.sidePots[1]['players']
assert 5 in holdemTable.sidePots[1]['players']
assert holdemTable.sidePots[2]['money'] == (99900) * 3
assert len(holdemTable.sidePots[2]['players']) == 3
assert 2 in holdemTable.sidePots[2]['players']
assert 3 in holdemTable.sidePots[2]['players']
assert 5 in holdemTable.sidePots[2]['players']
assert holdemTable.sidePots[3]['money'] == (2000) * 2
assert len(holdemTable.sidePots[3]['players']) == 2
assert 2 in holdemTable.sidePots[3]['players']
assert 3 in holdemTable.sidePots[3]['players']
result = holdemTable.end()
assert len(result) == 2
assert result[4] == (holdemTable.ante + 1000) * 5 + 400
assert result[2] == 303700
def test_Story4():
class MemberTest:
id = 1
owner = MemberTest()
holdemTable = HoldemTable(None, owner)
holdemTable.addPlayer(1)
holdemTable.addPlayer(2)
holdemTable.addPlayer(3)
holdemTable.addPlayer(4)
holdemTable.addPlayer(5)
holdemTable.gameStart()
holdemTable.allIn(1, 20000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 2
holdemTable.allIn(2, 120000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 3
holdemTable.allIn(3, 1300)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 4
holdemTable.allIn(4, 2000)
assert holdemTable.toNext() is False
assert holdemTable.whosTurn == 5
holdemTable.allIn(5, 1500)
assert holdemTable.toNext() is True
assert holdemTable.whosTurn is None
holdemTable.flop()
holdemTable.turnOrRiver()
holdemTable.turnOrRiver()
sortedUserIDList = holdemTable.getSortedPlayerIDList()
assert sortedUserIDList[0] == 3
assert sortedUserIDList[1] == 5
assert sortedUserIDList[2] == 4
assert sortedUserIDList[3] == 1
assert sortedUserIDList[4] == 2
def test_getSortedIDList():
class MemberTest:
id = 1
owner = MemberTest()
holdemTable = HoldemTable(None, owner)
holdemTable.addPlayer(1)
holdemTable.addPlayer(2)
holdemTable.addPlayer(3)
holdemTable.addPlayer(4)
holdemTable.addPlayer(5)
holdemTable.gameStart()
holdemTable.players[1]['cards'] = [Card(1, 2), Card(1, 3)]
holdemTable.players[2]['cards'] = [Card(3, 5), Card(2, 1)]
holdemTable.players[3]['cards'] = [Card(3, 10), Card(3, 11)]
holdemTable.players[4]['cards'] = [Card(2, 5), Card(2, 7)]
holdemTable.players[5]['cards'] = [Card(3, 5), Card(2, 2)]
holdemTable.board = [
Card(1, 5),
Card(1, 7),
Card(1, 10),
Card(1, 1),
Card(2, 3)
]
winner = holdemTable.getWinner()
assert len(winner) == 1
assert winner[0] == 1
| 34.682657
| 79
| 0.696085
| 2,082
| 18,798
| 6.28194
| 0.04659
| 0.301552
| 0.138925
| 0.151005
| 0.914825
| 0.894487
| 0.862528
| 0.857023
| 0.857023
| 0.851518
| 0
| 0.053405
| 0.196138
| 18,798
| 542
| 80
| 34.682657
| 0.812124
| 0.012501
| 0
| 0.825532
| 0
| 0
| 0.039957
| 0
| 0
| 0
| 0
| 0
| 0.553191
| 1
| 0.012766
| false
| 0
| 0.004255
| 0
| 0.042553
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
ae851c38be1a4044391bde1c444417a149235387
| 1,073
|
py
|
Python
|
test/test_tickbase.py
|
ironss/micropython-lib
|
61719636dad9aaa581c8e39e71ccc515e75c2d43
|
[
"MIT"
] | null | null | null |
test/test_tickbase.py
|
ironss/micropython-lib
|
61719636dad9aaa581c8e39e71ccc515e75c2d43
|
[
"MIT"
] | null | null | null |
test/test_tickbase.py
|
ironss/micropython-lib
|
61719636dad9aaa581c8e39e71ccc515e75c2d43
|
[
"MIT"
] | 2
|
2019-09-24T13:36:55.000Z
|
2020-04-18T02:05:38.000Z
|
import tickbase
import utime
def test_tickbase_zero_delay():
_action_count = 0
def action(ticks_now):
nonlocal _action_count
_action_count += 1
ticker = tickbase.Ticked_action_ms('test', action, 1000, 0)
assert(_action_count == 0)
ticker.step()
assert(_action_count == 1)
utime.sleep_ms(999)
ticker.step()
assert(_action_count == 1)
utime.sleep_ms(1)
ticker.step()
assert(_action_count == 2)
def test_tickbase_with_delay():
_action_count = 0
def action(ticks_now):
nonlocal _action_count
_action_count += 1
ticker = tickbase.Ticked_action_ms('test', action, 1000, 500, drifter=False)
assert(_action_count == 0)
ticker.step()
assert(_action_count == 0)
utime.sleep_ms(499)
ticker.step()
assert(_action_count == 0)
utime.sleep_ms(1)
ticker.step()
assert(_action_count == 1)
utime.sleep_ms(999)
ticker.step()
assert(_action_count == 1)
utime.sleep_ms(1)
ticker.step()
assert(_action_count == 2)
| 19.160714
| 80
| 0.640261
| 140
| 1,073
| 4.55
| 0.2
| 0.276295
| 0.266876
| 0.276295
| 0.871272
| 0.871272
| 0.871272
| 0.871272
| 0.871272
| 0.687598
| 0
| 0.047088
| 0.247903
| 1,073
| 55
| 81
| 19.509091
| 0.742255
| 0
| 0
| 0.815789
| 0
| 0
| 0.007463
| 0
| 0
| 0
| 0
| 0
| 0.263158
| 1
| 0.105263
| false
| 0
| 0.052632
| 0
| 0.157895
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
8826a6f039380f1a8e2e7dd5370e19bc98bb9391
| 14,027
|
py
|
Python
|
tests/src/python/test_qgslayoutmanagermodel.py
|
dyna-mis/Hilabeling
|
cb7d5d4be29624a20c8a367162dbc6fd779b2b52
|
[
"MIT"
] | null | null | null |
tests/src/python/test_qgslayoutmanagermodel.py
|
dyna-mis/Hilabeling
|
cb7d5d4be29624a20c8a367162dbc6fd779b2b52
|
[
"MIT"
] | null | null | null |
tests/src/python/test_qgslayoutmanagermodel.py
|
dyna-mis/Hilabeling
|
cb7d5d4be29624a20c8a367162dbc6fd779b2b52
|
[
"MIT"
] | 1
|
2021-12-25T08:40:30.000Z
|
2021-12-25T08:40:30.000Z
|
# -*- coding: utf-8 -*-
"""QGIS Unit tests for QgsLayoutManagerModel.
.. note:: This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
"""
__author__ = '(C) 2019 by Nyall Dawson'
__date__ = '11/03/2019'
__copyright__ = 'Copyright 2019, The QGIS Project'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '176c06ceefb5f555205e72b20c962740cc0ec183'
import qgis # NOQA
from qgis.PyQt.QtXml import QDomDocument
from qgis.core import (QgsPrintLayout,
QgsLayoutManager,
QgsLayoutManagerModel,
QgsLayoutManagerProxyModel,
QgsProject,
QgsReport,
QgsMasterLayoutInterface)
from qgis.PyQt.QtCore import Qt, QModelIndex
from qgis.testing import start_app, unittest
from utilities import unitTestDataPath
from qgis.PyQt.QtXml import QDomDocument
from qgis.PyQt.QtTest import QSignalSpy
start_app()
TEST_DATA_DIR = unitTestDataPath()
class TestQgsLayoutManagerModel(unittest.TestCase):
def setUp(self):
"""Run before each test."""
self.manager = None
self.aboutFired = False
def tearDown(self):
"""Run after each test."""
pass
def testModel(self):
project = QgsProject()
manager = QgsLayoutManager(project)
model = QgsLayoutManagerModel(manager)
self.assertEqual(model.rowCount(QModelIndex()), 0)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), None)
self.assertEqual(model.indexFromLayout(None), QModelIndex())
layout = QgsPrintLayout(project)
layout.setName('test layout')
self.assertEqual(model.indexFromLayout(layout), QModelIndex())
self.assertTrue(manager.addLayout(layout))
self.assertEqual(model.rowCount(QModelIndex()), 1)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), 'test layout')
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), layout)
self.assertEqual(model.indexFromLayout(layout), model.index(0, 0, QModelIndex()))
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.layoutFromIndex(model.index(1, 0, QModelIndex())), None)
layout.setName('test Layout')
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), 'test Layout')
layout2 = QgsPrintLayout(project)
layout2.setName('test layout2')
self.assertTrue(manager.addLayout(layout2))
self.assertEqual(model.rowCount(QModelIndex()), 2)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), 'test Layout')
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), layout)
self.assertEqual(model.indexFromLayout(layout), model.index(0, 0, QModelIndex()))
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), Qt.DisplayRole), 'test layout2')
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
self.assertEqual(model.layoutFromIndex(model.index(1, 0, QModelIndex())), layout2)
self.assertEqual(model.indexFromLayout(layout2), model.index(1, 0, QModelIndex()))
manager.removeLayout(layout)
self.assertEqual(model.rowCount(QModelIndex()), 1)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), 'test layout2')
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), layout2)
self.assertEqual(model.layoutFromIndex(model.index(1, 0, QModelIndex())), None)
self.assertEqual(model.indexFromLayout(layout2), model.index(0, 0, QModelIndex()))
manager.clear()
self.assertEqual(model.rowCount(QModelIndex()), 0)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), None)
# with empty row
model.setAllowEmptyLayout(True)
self.assertEqual(model.rowCount(QModelIndex()), 1)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), None)
layout = QgsPrintLayout(project)
layout.setName('test layout')
self.assertTrue(manager.addLayout(layout))
self.assertEqual(model.rowCount(QModelIndex()), 2)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), Qt.DisplayRole), 'test layout')
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(model.data(model.index(2, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(2, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), None)
self.assertEqual(model.layoutFromIndex(model.index(1, 0, QModelIndex())), layout)
self.assertEqual(model.indexFromLayout(layout), model.index(1, 0, QModelIndex()))
layout2 = QgsPrintLayout(project)
layout2.setName('test layout2')
self.assertTrue(manager.addLayout(layout2))
self.assertEqual(model.rowCount(QModelIndex()), 3)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), Qt.DisplayRole), 'test layout')
self.assertEqual(model.data(model.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(model.data(model.index(2, 0, QModelIndex()), Qt.DisplayRole), 'test layout2')
self.assertEqual(model.data(model.index(2, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), None)
self.assertEqual(model.layoutFromIndex(model.index(1, 0, QModelIndex())), layout)
self.assertEqual(model.layoutFromIndex(model.index(2, 0, QModelIndex())), layout2)
self.assertEqual(model.indexFromLayout(layout), model.index(1, 0, QModelIndex()))
self.assertEqual(model.indexFromLayout(layout2), model.index(2, 0, QModelIndex()))
manager.clear()
self.assertEqual(model.rowCount(QModelIndex()), 1)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(model.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(model.layoutFromIndex(model.index(0, 0, QModelIndex())), None)
def testProxyModel(self):
project = QgsProject()
manager = QgsLayoutManager(project)
model = QgsLayoutManagerModel(manager)
proxy = QgsLayoutManagerProxyModel()
proxy.setSourceModel(model)
self.assertEqual(proxy.rowCount(QModelIndex()), 0)
self.assertEqual(proxy.data(model.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(proxy.data(model.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
layout = QgsPrintLayout(project)
layout.setName('ccc')
self.assertTrue(manager.addLayout(layout))
self.assertEqual(proxy.rowCount(QModelIndex()), 1)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), Qt.DisplayRole), 'ccc')
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
layout2 = QgsPrintLayout(project)
layout2.setName('bbb')
self.assertTrue(manager.addLayout(layout2))
self.assertEqual(proxy.rowCount(QModelIndex()), 2)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), Qt.DisplayRole), 'bbb')
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), Qt.DisplayRole), 'ccc')
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
layout.setName('aaa')
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), Qt.DisplayRole), 'aaa')
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), Qt.DisplayRole), 'bbb')
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
model.setAllowEmptyLayout(True)
self.assertEqual(proxy.rowCount(QModelIndex()), 3)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), Qt.DisplayRole), 'aaa')
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(proxy.data(proxy.index(2, 0, QModelIndex()), Qt.DisplayRole), 'bbb')
self.assertEqual(proxy.data(proxy.index(2, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
r = QgsReport(project)
r.setName('ddd')
manager.addLayout(r)
self.assertEqual(proxy.rowCount(QModelIndex()), 4)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), Qt.DisplayRole), 'aaa')
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(proxy.data(proxy.index(2, 0, QModelIndex()), Qt.DisplayRole), 'bbb')
self.assertEqual(proxy.data(proxy.index(2, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
self.assertEqual(proxy.data(proxy.index(3, 0, QModelIndex()), Qt.DisplayRole), 'ddd')
self.assertEqual(proxy.data(proxy.index(3, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), r)
proxy.setFilters(QgsLayoutManagerProxyModel.FilterPrintLayouts)
self.assertEqual(proxy.filters(), QgsLayoutManagerProxyModel.FilterPrintLayouts)
self.assertEqual(proxy.rowCount(QModelIndex()), 3)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), Qt.DisplayRole), 'aaa')
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout)
self.assertEqual(proxy.data(proxy.index(2, 0, QModelIndex()), Qt.DisplayRole), 'bbb')
self.assertEqual(proxy.data(proxy.index(2, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), layout2)
proxy.setFilters(QgsLayoutManagerProxyModel.FilterReports)
self.assertEqual(proxy.filters(), QgsLayoutManagerProxyModel.FilterReports)
self.assertEqual(proxy.rowCount(QModelIndex()), 2)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), Qt.DisplayRole), None)
self.assertEqual(proxy.data(proxy.index(0, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), None)
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), Qt.DisplayRole), 'ddd')
self.assertEqual(proxy.data(proxy.index(1, 0, QModelIndex()), QgsLayoutManagerModel.LayoutRole), r)
proxy.setFilters(QgsLayoutManagerProxyModel.FilterPrintLayouts | QgsLayoutManagerProxyModel.FilterReports)
self.assertEqual(proxy.filters(), QgsLayoutManagerProxyModel.FilterPrintLayouts | QgsLayoutManagerProxyModel.FilterReports)
self.assertEqual(proxy.rowCount(QModelIndex()), 4)
if __name__ == '__main__':
unittest.main()
| 61.253275
| 131
| 0.703073
| 1,526
| 14,027
| 6.444299
| 0.09502
| 0.175412
| 0.134228
| 0.086028
| 0.88672
| 0.868416
| 0.850824
| 0.785438
| 0.766118
| 0.719443
| 0
| 0.023356
| 0.157553
| 14,027
| 228
| 132
| 61.52193
| 0.808835
| 0.031653
| 0
| 0.570652
| 0
| 0
| 0.023077
| 0.002949
| 0
| 0
| 0
| 0
| 0.657609
| 1
| 0.021739
| false
| 0.005435
| 0.043478
| 0
| 0.070652
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
8850c4fce563846dbf094ce44aeac74ad1fc2675
| 125
|
py
|
Python
|
OpenMatch/data/tokenizers/__init__.py
|
vishalbelsare/OpenMatch
|
84b25502bf52c58b9e71bd0754b2fc192d9b448f
|
[
"MIT"
] | 403
|
2020-01-17T06:54:46.000Z
|
2022-03-30T05:47:42.000Z
|
OpenMatch/data/tokenizers/__init__.py
|
vishalbelsare/OpenMatch
|
84b25502bf52c58b9e71bd0754b2fc192d9b448f
|
[
"MIT"
] | 30
|
2020-06-07T12:28:07.000Z
|
2022-03-20T05:26:03.000Z
|
OpenMatch/data/tokenizers/__init__.py
|
vishalbelsare/OpenMatch
|
84b25502bf52c58b9e71bd0754b2fc192d9b448f
|
[
"MIT"
] | 48
|
2020-07-15T09:45:46.000Z
|
2022-03-01T07:27:59.000Z
|
from OpenMatch.data.tokenizers.tokenizer import Tokenizer
from OpenMatch.data.tokenizers.word_tokenizer import WordTokenizer
| 41.666667
| 66
| 0.888
| 15
| 125
| 7.333333
| 0.533333
| 0.236364
| 0.309091
| 0.490909
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.064
| 125
| 2
| 67
| 62.5
| 0.940171
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
88761fe6a1f7b0a625cdd22de94047181238883f
| 10,970
|
py
|
Python
|
nipy/neurospin/utils/simul_multisubject_fmri_dataset.py
|
fperez/nipy
|
559f17150bd9fa8ead4fd088b330d7cf7db7aa79
|
[
"BSD-3-Clause"
] | 1
|
2015-05-07T16:53:33.000Z
|
2015-05-07T16:53:33.000Z
|
nipy/neurospin/utils/simul_multisubject_fmri_dataset.py
|
fperez/nipy
|
559f17150bd9fa8ead4fd088b330d7cf7db7aa79
|
[
"BSD-3-Clause"
] | null | null | null |
nipy/neurospin/utils/simul_multisubject_fmri_dataset.py
|
fperez/nipy
|
559f17150bd9fa8ead4fd088b330d7cf7db7aa79
|
[
"BSD-3-Clause"
] | null | null | null |
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
This module conatins a function to produce a dataset which simulates
a collection of 2D images This dataset is saved as a 3D image
(each slice being a subject) and a 3D array
example of use: surrogate_2d_dataset(nbsubj=1,fid="/tmp/toto.dat",verbose=1)
todo: rewrite it as a class
Author : Bertrand Thirion, 2008-2009
"""
import numpy as np
import scipy.ndimage as nd
from nipy.io.imageformats import save, Nifti1Image
# definition of the maxima at the group level
pos = np.array([[6 , 7],
[10, 10],
[15, 10]])
ampli = np.array([3, 4, 4])
def _cone2d(shape, ij, pos, ampli, width):
"""
Define a cone of the proposed grid
"""
temp = np.zeros(shape)
pos = np.reshape(pos,(1,2))
dist = np.sqrt(np.sum((ij-pos)**2, axis=1))
codi = (width-dist)*(dist < width)/width
temp[ij[:,0],ij[:,1]] = codi*ampli
return temp
def _cone3d(shape, ij, pos, ampli, width):
"""
Define a cone of the proposed grid
"""
temp = np.zeros(shape)
pos = np.reshape(pos,(1,3))
dist = np.sqrt(np.sum((ij-pos)**2, axis=1))
codi = (width-dist)*(dist < width)/width
temp[ij[:,0],ij[:,1],ij[:,2]] = codi*ampli
return temp
def surrogate_2d_dataset(nbsubj=10, dimx=30, dimy=30, sk=1.0,
noise_level=1.0, pos=pos, ampli=ampli,
spatial_jitter=1.0, signal_jitter=1.0,
width=5.0, out_text_file=None, out_image_file=None,
verbose=False, seed=False):
"""
Create surrogate (simulated) 2D activation data with spatial noise.
Parameters
-----------
nbsubj: integer, optionnal
The number of subjects, ie the number of different maps
generated.
dimx: integer, optionnal
The x size of the array returned.
dimy: integer
The y size of the array returned.
sk: float, optionnal
Amount of spatial noise smoothness.
noise_level: float, optionnal
Amplitude of the spatial noise.
amplitude=noise_level)
pos: 2D ndarray of integers, optionnal
x, y positions of the various simulated activations.
ampli: 1D ndarray of floats, optionnal
Respective amplitude of each activation
spatial_jitter: float, optionnal
Random spatial jitter added to the position of each activation,
in pixel.
signal_jitter: float, optionnal
Random amplitude fluctuation for each activation, added to the
amplitude specified by ampli
width: float or ndarray, optionnal
Width of the activations
out_text_file: string or None, optionnal
If not None, the resulting array is saved as a text file with the
given file name
out_image_file: string or None, optionnal
If not None, the resulting is saved as a nifti file with the
given file name.
verbose: boolean, optionnal
If verbose is true, the data for the last subject is plotted as
a 2D image.
seed=False: int, optionnal
If seed is not False, the random number generator is initialized
at a certain value
Returns
-------
dataset: 3D ndarray
The surrogate activation map, with dimensions (nbsubj, dimx, dimy)
"""
if seed:
nr = np.random.RandomState([seed])
else:
import numpy.random as nr
shape = (dimx, dimy)
ij = np.array(np.where(np.ones(shape))).T
dataset = []
for s in range(nbsubj):
# make the signal
data = np.zeros(shape)
lpos = pos + spatial_jitter*nr.randn(1, 2)
lampli = ampli + signal_jitter*nr.randn(np.size(ampli))
for k in range(np.size(lampli)):
data = np.maximum(data,
_cone2d(shape, ij, lpos[k], lampli[k], width))
# make some noise
noise = nr.randn(dimx,dimy)
# smooth the noise
noise = nd.gaussian_filter(noise, sk)
noise = np.reshape(noise, (-1, 1))
noise *= noise_level/np.std(noise)
#make the mixture
data += np.reshape(noise, shape)
dataset.append(data)
if verbose:
import matplotlib.pylab as mp
mp.figure()
mp.imshow(data, interpolation='nearest')
mp.colorbar()
dataset = np.array(dataset)
if out_text_file is not None:
dataset.tofile(out_text_file)
if out_image_file is not None:
from nipy.io.imageformats import save, Nifti1Image
save(Nifti1Image( dataset, np.eye(4)), out_image_file)
return dataset
def surrogate_3d_dataset(nbsubj=1, shape=(20,20,20), mask=None,
sk=1.0, noise_level=1.0, pos=None, ampli=None,
spatial_jitter=1.0, signal_jitter=1.0,
width=5.0, out_text_file=None, out_image_file=None,
verbose=False, seed=False):
"""
Create surrogate (simulated) 3D activation data with spatial noise.
Parameters
-----------
nbsubj: integer, optionnal
The number of subjects, ie the number of different maps
generated.
shape=(20,20,20): tuple of integers,
the shape of each image
mask=None: brifti image instance,
referential- and mask- defining image (overrides shape)
sk: float, optionnal
Amount of spatial noise smoothness.
noise_level: float, optionnal
Amplitude of the spatial noise.
amplitude=noise_level)
pos: 2D ndarray of integers, optionnal
x, y positions of the various simulated activations.
ampli: 1D ndarray of floats, optionnal
Respective amplitude of each activation
spatial_jitter: float, optionnal
Random spatial jitter added to the position of each activation,
in pixel.
signal_jitter: float, optionnal
Random amplitude fluctuation for each activation, added to the
amplitude specified by ampli
width: float or ndarray, optionnal
Width of the activations
out_text_file: string or None, optionnal
If not None, the resulting array is saved as a text file with the
given file name
out_image_file: string or None, optionnal
If not None, the resulting is saved as a nifti file with the
given file name.
verbose: boolean, optionnal
If verbose is true, the data for the last subject is plotted as
a 2D image.
seed=False: int, optionnal
If seed is not False, the random number generator is initialized
at a certain value
Returns
-------
dataset: 3D ndarray
The surrogate activation map, with dimensions (nbsubj, dimx, dimy, dimz)
"""
if seed:
nr = np.random.RandomState([seed])
else:
import numpy.random as nr
if mask is not None:
shape = mask.get_shape()
mask_data = mask.get_data()
else:
mask_data = np.ones(shape)
ijk = np.array(np.where(mask_data)).T
dataset = []
# make the signal
for s in range(nbsubj):
data = np.zeros(shape)
if pos !=None:
if len(pos)!=len(ampli):
raise ValueError, 'ampli and pos do not have the same len'
lpos = pos + spatial_jitter*nr.randn(1, 3)
lampli = ampli + signal_jitter*nr.randn(np.size(ampli))
for k in range(np.size(lampli)):
data = np.maximum(data,_cone3d(shape, ijk, lpos[k], lampli[k],
width))
# make some noise
noise = nr.randn(shape[0], shape[1], shape[2])
# smooth the noise
noise = nd.gaussian_filter(noise, sk)
#noise = np.reshape(noise, (-1, 1))
noise *= noise_level/np.std(noise)
#make the mixture
data += noise
dataset.append(data)
if verbose:
import matplotlib.pylab as mp
mp.figure()
mp.imshow(data, interpolation='nearest')
mp.colorbar()
dataset = np.array(dataset)
if out_text_file is not None:
dataset.tofile(out_text_file)
if out_image_file is not None:
save(Nifti1Image( dataset, np.eye(4)), out_image_file)
return dataset
def surrogate_4d_dataset(shape=(20,20,20), mask=None, n_scans=1, dmtx=None,
sk=1.0, noise_level=1.0, out_image_file=None,
verbose=False, seed=False):
"""
Create surrogate (simulated) 3D activation data with spatial noise.
Parameters
-----------
shape=(20,20,20): tuple of integers,
the shape of each image
mask=None: brifti image instance,
referential- and mask- defining image (overrides shape)
n_scans: int, optional,
number of scans to be simlulated
overrided by the design matrix
dmtx: arrau of shape(n_scans, n_rows),
the design matrix
sk: float, optionnal
Amount of spatial noise smoothness.
noise_level: float, optionnal
Amplitude of the spatial noise.
amplitude=noise_level)
out_image_file: string or None, optionnal
If not None, the resulting is saved as a nifti file with the
given file name.
verbose: boolean, optionnal
If verbose is true, the data for the last subject is plotted as
a 2D image.
seed=False: int, optionnal
If seed is not False, the random number generator is initialized
at a certain value
Returns
-------
dataset: ndarray of shape (shape[0], shape[1], shape[2], n_scans)
The surrogate activation map, with dimensions (nbsubj, dimx, dimy, dimz)
"""
if seed:
nr = np.random.RandomState([seed])
else:
import numpy.random as nr
if mask is not None:
shape = mask.get_shape()
affine = mask.get_affine()
mask_data = mask.get_data().astype('bool')
else:
affine = np.eye(4)
mask_data = np.ones(shape).astype('bool')
if dmtx is not None:
n_scans = dmtx.shape[0]
shape_4d = tuple((shape[0], shape[1], shape[2], n_scans))
data = np.zeros(shape_4d)
# make the signal
if dmtx is not None:
for r in range(dmtx.shape[1]):
beta = nd.gaussian_filter(nr.randn(*shape),sk)
beta /= np.std(beta)
data[mask_data,:] += np.outer(beta[mask_data],dmtx[:,r])
for s in range(n_scans):
# make some noise
noise = nr.randn(shape[0], shape[1], shape[2])
# smooth the noise
noise = nd.gaussian_filter(noise, sk)
noise *= noise_level/np.std(noise)
#make the mixture
data[:,:,:,s] += noise
data[:,:,:,s] += 100*mask_data
wim = Nifti1Image( data, affine)
if out_image_file is not None:
save(wim, out_image_file)
return wim
| 32.551929
| 80
| 0.608933
| 1,506
| 10,970
| 4.367198
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| 0.0149
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| 0.803254
| 0.768588
| 0.763418
| 0.741828
| 0.720997
| 0.720997
| 0
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| 0.296536
| 10,970
| 336
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0
| 7
|
88859471d12af6e950e863a4e56eb529e40948e0
| 221,856
|
py
|
Python
|
backup/11.skimage17.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | null | null | null |
backup/11.skimage17.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | 3
|
2019-12-26T05:13:55.000Z
|
2020-03-07T06:59:56.000Z
|
backup/11.skimage17.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | null | null | null |
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import skimage.io\n",
"from skimage import color\n",
"from skimage.util import view_as_blocks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load an image in gray-scale"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert a color <b>RGB image</b> into <b>gray-scale image</b>\n",
"\n",
"Photo by Nancy Nobody from Pexels: https://www.pexels.com/photo/photography-of-three-dogs-looking-up-850602/"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x82033fe48>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"three_dogs = skimage.io.imread(\"./images/pexels-3-dogs.jpg\")\n",
"\n",
"three_dogs = color.rgb2gray(three_dogs)\n",
"\n",
"plt.imshow(three_dogs, cmap=\"gray\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(344, 516)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"three_dogs.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Square block of 4x4"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Change this to (43, 43) and re-run all cells and show images\n",
"block_shape = (4, 4)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"three_dogs_blocks = view_as_blocks(three_dogs, block_shape) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(86, 129, 4, 4)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"three_dogs_blocks.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Collapse 4x4 block into 1D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Flatten the image in such way that the data in pixels should not change (-1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of the blocks image : (86, 129, 4, 4)\n",
"Shape of the flattened image : (86, 129, 16)\n"
]
}
],
"source": [
"flattened_blocks = three_dogs_blocks.reshape(three_dogs_blocks.shape[0], three_dogs_blocks.shape[1], -1)\n",
"\n",
"print('Shape of the blocks image :', three_dogs_blocks.shape)\n",
"\n",
"print('Shape of the flattened image :', flattened_blocks.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mean pooling"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x820925be0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mean_blocks = np.mean(flattened_blocks, axis=2)\n",
"\n",
"plt.imshow(mean_blocks, interpolation='nearest', cmap='gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Max pooling"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x82098bf98>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"max_blocks = np.max(flattened_blocks, axis=2)\n",
"\n",
"plt.imshow(max_blocks, interpolation='nearest', cmap='gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Median pooling"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x820c71390>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"median_blocks = np.median(flattened_blocks, axis=2)\n",
"\n",
"plt.imshow(median_blocks, interpolation='nearest', cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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0
| 7
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889197b46fee08bf0601c8869891dd9d4d0908e8
| 187,661
|
py
|
Python
|
opcua/server/standard_address_space/standard_address_space_part4.py
|
aixiwang/opcua2cloud
|
32e1e745e4939f8d4fd51892d9a51230ffdfc198
|
[
"Apache-2.0"
] | null | null | null |
opcua/server/standard_address_space/standard_address_space_part4.py
|
aixiwang/opcua2cloud
|
32e1e745e4939f8d4fd51892d9a51230ffdfc198
|
[
"Apache-2.0"
] | null | null | null |
opcua/server/standard_address_space/standard_address_space_part4.py
|
aixiwang/opcua2cloud
|
32e1e745e4939f8d4fd51892d9a51230ffdfc198
|
[
"Apache-2.0"
] | 2
|
2019-01-14T10:13:57.000Z
|
2020-02-11T15:22:14.000Z
|
# -*- coding: utf-8 -*-
"""
DO NOT EDIT THIS FILE!
It is automatically generated from opcfoundation.org schemas.
"""
from opcua import ua
from opcua.ua import NodeId, QualifiedName, NumericNodeId, StringNodeId, GuidNodeId
from opcua.ua import NodeClass, LocalizedText
def create_standard_address_space_Part4(server):
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(18, 0)
node.BrowseName = QualifiedName('ExpandedNodeId', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(24, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Describes a value that is an absolute identifier for a node.")
attrs.DisplayName = LocalizedText("ExpandedNodeId")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(18, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(24, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(19, 0)
node.BrowseName = QualifiedName('StatusCode', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(24, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Describes a value that is a code representing the outcome of an operation by a Server.")
attrs.DisplayName = LocalizedText("StatusCode")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(19, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(24, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(23, 0)
node.BrowseName = QualifiedName('DataValue', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(24, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Describes a value that is a structure containing a value, a status code and timestamps.")
attrs.DisplayName = LocalizedText("DataValue")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(23, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(24, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(25, 0)
node.BrowseName = QualifiedName('DiagnosticInfo', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(24, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Describes a value that is a structure containing diagnostics associated with a StatusCode.")
attrs.DisplayName = LocalizedText("DiagnosticInfo")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(25, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(24, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(288, 0)
node.BrowseName = QualifiedName('IntegerId', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(7, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A numeric identifier for an object.")
attrs.DisplayName = LocalizedText("IntegerId")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(288, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(307, 0)
node.BrowseName = QualifiedName('ApplicationType', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("The types of applications.")
attrs.DisplayName = LocalizedText("ApplicationType")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(307, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7597, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(307, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(7597, 0)
node.BrowseName = QualifiedName('EnumStrings', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(307, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumStrings")
attrs.DataType = ua.NodeId(ua.ObjectIds.LocalizedText)
attrs.Value = [LocalizedText('Server'),LocalizedText('Client'),LocalizedText('ClientAndServer'),LocalizedText('DiscoveryServer')]
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(7597, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(7597, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(7597, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(307, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(308, 0)
node.BrowseName = QualifiedName('ApplicationDescription', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Describes an application and how to find it.")
attrs.DisplayName = LocalizedText("ApplicationDescription")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(308, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(20998, 0)
node.BrowseName = QualifiedName('VersionTime', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(7, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("VersionTime")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(20998, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12189, 0)
node.BrowseName = QualifiedName('ServerOnNetwork', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("ServerOnNetwork")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(12189, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(311, 0)
node.BrowseName = QualifiedName('ApplicationInstanceCertificate', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(15, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A certificate for an instance of an application.")
attrs.DisplayName = LocalizedText("ApplicationInstanceCertificate")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(311, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(15, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(302, 0)
node.BrowseName = QualifiedName('MessageSecurityMode', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("The type of security to use on a message.")
attrs.DisplayName = LocalizedText("MessageSecurityMode")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(302, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7595, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(302, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(7595, 0)
node.BrowseName = QualifiedName('EnumStrings', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(302, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumStrings")
attrs.DataType = ua.NodeId(ua.ObjectIds.LocalizedText)
attrs.Value = [LocalizedText('Invalid'),LocalizedText('None'),LocalizedText('Sign'),LocalizedText('SignAndEncrypt')]
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(7595, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(7595, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(7595, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(302, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(303, 0)
node.BrowseName = QualifiedName('UserTokenType', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("The possible user token types.")
attrs.DisplayName = LocalizedText("UserTokenType")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(303, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7596, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(303, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(7596, 0)
node.BrowseName = QualifiedName('EnumStrings', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(303, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumStrings")
attrs.DataType = ua.NodeId(ua.ObjectIds.LocalizedText)
attrs.Value = [LocalizedText('Anonymous'),LocalizedText('UserName'),LocalizedText('Certificate'),LocalizedText('IssuedToken')]
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(7596, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(7596, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(7596, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(303, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(304, 0)
node.BrowseName = QualifiedName('UserTokenPolicy', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Describes a user token that can be used with a server.")
attrs.DisplayName = LocalizedText("UserTokenPolicy")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(304, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(312, 0)
node.BrowseName = QualifiedName('EndpointDescription', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("The description of a endpoint that can be used to access a server.")
attrs.DisplayName = LocalizedText("EndpointDescription")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(312, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(432, 0)
node.BrowseName = QualifiedName('RegisteredServer', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("The information required to register a server with a discovery server.")
attrs.DisplayName = LocalizedText("RegisteredServer")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(432, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12890, 0)
node.BrowseName = QualifiedName('DiscoveryConfiguration', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A base type for discovery configuration information.")
attrs.DisplayName = LocalizedText("DiscoveryConfiguration")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(12890, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12891, 0)
node.BrowseName = QualifiedName('MdnsDiscoveryConfiguration', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(12890, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("The discovery information needed for mDNS registration.")
attrs.DisplayName = LocalizedText("MdnsDiscoveryConfiguration")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(12891, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12890, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(315, 0)
node.BrowseName = QualifiedName('SecurityTokenRequestType', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Indicates whether a token if being created or renewed.")
attrs.DisplayName = LocalizedText("SecurityTokenRequestType")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(315, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7598, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(315, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(7598, 0)
node.BrowseName = QualifiedName('EnumStrings', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(315, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumStrings")
attrs.DataType = ua.NodeId(ua.ObjectIds.LocalizedText)
attrs.Value = [LocalizedText('Issue'),LocalizedText('Renew')]
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(7598, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(7598, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(7598, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(315, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(344, 0)
node.BrowseName = QualifiedName('SignedSoftwareCertificate', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A software certificate with a digital signature.")
attrs.DisplayName = LocalizedText("SignedSoftwareCertificate")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(344, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(388, 0)
node.BrowseName = QualifiedName('SessionAuthenticationToken', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(17, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A unique identifier for a session used to authenticate requests.")
attrs.DisplayName = LocalizedText("SessionAuthenticationToken")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(388, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(17, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(316, 0)
node.BrowseName = QualifiedName('UserIdentityToken', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A base type for a user identity token.")
attrs.DisplayName = LocalizedText("UserIdentityToken")
attrs.IsAbstract = True
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(316, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(319, 0)
node.BrowseName = QualifiedName('AnonymousIdentityToken', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(316, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A token representing an anonymous user.")
attrs.DisplayName = LocalizedText("AnonymousIdentityToken")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(319, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(316, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(322, 0)
node.BrowseName = QualifiedName('UserNameIdentityToken', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(316, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A token representing a user identified by a user name and password.")
attrs.DisplayName = LocalizedText("UserNameIdentityToken")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(322, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(316, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(325, 0)
node.BrowseName = QualifiedName('X509IdentityToken', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(316, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A token representing a user identified by an X509 certificate.")
attrs.DisplayName = LocalizedText("X509IdentityToken")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(325, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(316, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(938, 0)
node.BrowseName = QualifiedName('IssuedIdentityToken', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(316, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A token representing a user identified by a WS-Security XML token.")
attrs.DisplayName = LocalizedText("IssuedIdentityToken")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(938, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(316, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(348, 0)
node.BrowseName = QualifiedName('NodeAttributesMask', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("The bits used to specify default attributes for a new node.")
attrs.DisplayName = LocalizedText("NodeAttributesMask")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(348, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(11881, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(348, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(11881, 0)
node.BrowseName = QualifiedName('EnumValues', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(348, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumValues")
attrs.DataType = NumericNodeId(7594, 0)
value = []
extobj = ua.EnumValueType()
extobj.Value = 0
extobj.DisplayName.Text = 'None'
extobj.Description.Text = 'No attribuites provided.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 1
extobj.DisplayName.Text = 'AccessLevel'
extobj.Description.Text = 'The access level attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 2
extobj.DisplayName.Text = 'ArrayDimensions'
extobj.Description.Text = 'The array dimensions attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 4
extobj.DisplayName.Text = 'BrowseName'
extobj.Description.Text = 'The browse name attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 8
extobj.DisplayName.Text = 'ContainsNoLoops'
extobj.Description.Text = 'The contains no loops attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 16
extobj.DisplayName.Text = 'DataType'
extobj.Description.Text = 'The data type attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 32
extobj.DisplayName.Text = 'Description'
extobj.Description.Text = 'The description attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 64
extobj.DisplayName.Text = 'DisplayName'
extobj.Description.Text = 'The display name attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 128
extobj.DisplayName.Text = 'EventNotifier'
extobj.Description.Text = 'The event notifier attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 256
extobj.DisplayName.Text = 'Executable'
extobj.Description.Text = 'The executable attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 512
extobj.DisplayName.Text = 'Historizing'
extobj.Description.Text = 'The historizing attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 1024
extobj.DisplayName.Text = 'InverseName'
extobj.Description.Text = 'The inverse name attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 2048
extobj.DisplayName.Text = 'IsAbstract'
extobj.Description.Text = 'The is abstract attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 4096
extobj.DisplayName.Text = 'MinimumSamplingInterval'
extobj.Description.Text = 'The minimum sampling interval attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 8192
extobj.DisplayName.Text = 'NodeClass'
extobj.Description.Text = 'The node class attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 16384
extobj.DisplayName.Text = 'NodeId'
extobj.Description.Text = 'The node id attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 32768
extobj.DisplayName.Text = 'Symmetric'
extobj.Description.Text = 'The symmetric attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 65536
extobj.DisplayName.Text = 'UserAccessLevel'
extobj.Description.Text = 'The user access level attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 131072
extobj.DisplayName.Text = 'UserExecutable'
extobj.Description.Text = 'The user executable attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 262144
extobj.DisplayName.Text = 'UserWriteMask'
extobj.Description.Text = 'The user write mask attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 524288
extobj.DisplayName.Text = 'ValueRank'
extobj.Description.Text = 'The value rank attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 1048576
extobj.DisplayName.Text = 'WriteMask'
extobj.Description.Text = 'The write mask attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 2097152
extobj.DisplayName.Text = 'Value'
extobj.Description.Text = 'The value attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 4194304
extobj.DisplayName.Text = 'DataTypeDefinition'
extobj.Description.Text = 'The write mask attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 8388608
extobj.DisplayName.Text = 'RolePermissions'
extobj.Description.Text = 'The write mask attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 16777216
extobj.DisplayName.Text = 'AccessRestrictions'
extobj.Description.Text = 'The write mask attribute is specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 33554431
extobj.DisplayName.Text = 'All'
extobj.Description.Text = 'All attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 26501220
extobj.DisplayName.Text = 'BaseNode'
extobj.Description.Text = 'All base attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 26501348
extobj.DisplayName.Text = 'Object'
extobj.Description.Text = 'All object attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 26503268
extobj.DisplayName.Text = 'ObjectType'
extobj.Description.Text = 'All object type attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 26571383
extobj.DisplayName.Text = 'Variable'
extobj.Description.Text = 'All variable attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 28600438
extobj.DisplayName.Text = 'VariableType'
extobj.Description.Text = 'All variable type attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 26632548
extobj.DisplayName.Text = 'Method'
extobj.Description.Text = 'All method attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 26537060
extobj.DisplayName.Text = 'ReferenceType'
extobj.Description.Text = 'All reference type attributes are specified.'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 26501356
extobj.DisplayName.Text = 'View'
extobj.Description.Text = 'All view attributes are specified.'
value.append(extobj)
attrs.Value = ua.Variant(value, ua.VariantType.ExtensionObject)
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(11881, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(11881, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(11881, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(348, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(376, 0)
node.BrowseName = QualifiedName('AddNodesItem', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A request to add a node to the server address space.")
attrs.DisplayName = LocalizedText("AddNodesItem")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(376, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(379, 0)
node.BrowseName = QualifiedName('AddReferencesItem', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A request to add a reference to the server address space.")
attrs.DisplayName = LocalizedText("AddReferencesItem")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(379, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(382, 0)
node.BrowseName = QualifiedName('DeleteNodesItem', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A request to delete a node to the server address space.")
attrs.DisplayName = LocalizedText("DeleteNodesItem")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(382, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(385, 0)
node.BrowseName = QualifiedName('DeleteReferencesItem', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A request to delete a node from the server address space.")
attrs.DisplayName = LocalizedText("DeleteReferencesItem")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(385, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(347, 0)
node.BrowseName = QualifiedName('AttributeWriteMask', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(7, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Define bits used to indicate which attributes are writable.")
attrs.DisplayName = LocalizedText("AttributeWriteMask")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(347, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(15036, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(347, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15036, 0)
node.BrowseName = QualifiedName('OptionSetValues', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(347, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("OptionSetValues")
attrs.DataType = ua.NodeId(ua.ObjectIds.LocalizedText)
attrs.Value = [LocalizedText('AccessLevel'),LocalizedText('ArrayDimensions'),LocalizedText('BrowseName'),LocalizedText('ContainsNoLoops'),LocalizedText('DataType'),LocalizedText('Description'),LocalizedText('DisplayName'),LocalizedText('EventNotifier'),LocalizedText('Executable'),LocalizedText('Historizing'),LocalizedText('InverseName'),LocalizedText('IsAbstract'),LocalizedText('MinimumSamplingInterval'),LocalizedText('NodeClass'),LocalizedText('NodeId'),LocalizedText('Symmetric'),LocalizedText('UserAccessLevel'),LocalizedText('UserExecutable'),LocalizedText('UserWriteMask'),LocalizedText('ValueRank'),LocalizedText('WriteMask'),LocalizedText('ValueForVariableType'),LocalizedText('DataTypeDefinition'),LocalizedText('RolePermissions'),LocalizedText('AccessRestrictions'),LocalizedText('AccessLevelEx')]
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15036, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(15036, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(15036, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(347, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(521, 0)
node.BrowseName = QualifiedName('ContinuationPoint', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(15, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("An identifier for a suspended query or browse operation.")
attrs.DisplayName = LocalizedText("ContinuationPoint")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(521, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(15, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(537, 0)
node.BrowseName = QualifiedName('RelativePathElement', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("An element in a relative path.")
attrs.DisplayName = LocalizedText("RelativePathElement")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(537, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(540, 0)
node.BrowseName = QualifiedName('RelativePath', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A relative path constructed from reference types and browse names.")
attrs.DisplayName = LocalizedText("RelativePath")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(540, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(289, 0)
node.BrowseName = QualifiedName('Counter', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(7, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A monotonically increasing value.")
attrs.DisplayName = LocalizedText("Counter")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(289, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(291, 0)
node.BrowseName = QualifiedName('NumericRange', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(12, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("Specifies a range of array indexes.")
attrs.DisplayName = LocalizedText("NumericRange")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(291, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(292, 0)
node.BrowseName = QualifiedName('Time', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(12, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A time value specified as HH:MM:SS.SSS.")
attrs.DisplayName = LocalizedText("Time")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(292, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(293, 0)
node.BrowseName = QualifiedName('Date', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(13, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.Description = LocalizedText("A date value.")
attrs.DisplayName = LocalizedText("Date")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(293, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(13, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(331, 0)
node.BrowseName = QualifiedName('EndpointConfiguration', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("EndpointConfiguration")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(331, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(576, 0)
node.BrowseName = QualifiedName('FilterOperator', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("FilterOperator")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(576, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7605, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(576, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(7605, 0)
node.BrowseName = QualifiedName('EnumStrings', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(576, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumStrings")
attrs.DataType = ua.NodeId(ua.ObjectIds.LocalizedText)
attrs.Value = [LocalizedText('Equals'),LocalizedText('IsNull'),LocalizedText('GreaterThan'),LocalizedText('LessThan'),LocalizedText('GreaterThanOrEqual'),LocalizedText('LessThanOrEqual'),LocalizedText('Like'),LocalizedText('Not'),LocalizedText('Between'),LocalizedText('InList'),LocalizedText('And'),LocalizedText('Or'),LocalizedText('Cast'),LocalizedText('InView'),LocalizedText('OfType'),LocalizedText('RelatedTo'),LocalizedText('BitwiseAnd'),LocalizedText('BitwiseOr')]
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(7605, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(7605, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(7605, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(576, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(583, 0)
node.BrowseName = QualifiedName('ContentFilterElement', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("ContentFilterElement")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(583, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(586, 0)
node.BrowseName = QualifiedName('ContentFilter', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("ContentFilter")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(586, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(589, 0)
node.BrowseName = QualifiedName('FilterOperand', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("FilterOperand")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(589, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(592, 0)
node.BrowseName = QualifiedName('ElementOperand', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(589, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("ElementOperand")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(592, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(589, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(595, 0)
node.BrowseName = QualifiedName('LiteralOperand', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(589, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("LiteralOperand")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(595, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(589, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(598, 0)
node.BrowseName = QualifiedName('AttributeOperand', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(589, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("AttributeOperand")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(598, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(589, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(601, 0)
node.BrowseName = QualifiedName('SimpleAttributeOperand', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(589, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("SimpleAttributeOperand")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(601, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(589, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(659, 0)
node.BrowseName = QualifiedName('HistoryEvent', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("HistoryEvent")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(659, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(11234, 0)
node.BrowseName = QualifiedName('HistoryUpdateType', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("HistoryUpdateType")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(11234, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(11884, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(11234, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(11884, 0)
node.BrowseName = QualifiedName('EnumValues', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(11234, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumValues")
attrs.DataType = NumericNodeId(7594, 0)
value = []
extobj = ua.EnumValueType()
extobj.Value = 1
extobj.DisplayName.Text = 'Insert'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 2
extobj.DisplayName.Text = 'Replace'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 3
extobj.DisplayName.Text = 'Update'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 4
extobj.DisplayName.Text = 'Delete'
value.append(extobj)
attrs.Value = ua.Variant(value, ua.VariantType.ExtensionObject)
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(11884, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(11884, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(11884, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(11234, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(11293, 0)
node.BrowseName = QualifiedName('PerformUpdateType', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(29, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("PerformUpdateType")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(11293, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(11885, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(11293, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(29, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(11885, 0)
node.BrowseName = QualifiedName('EnumValues', 0)
node.NodeClass = NodeClass.Variable
node.ParentNodeId = NumericNodeId(11293, 0)
node.ReferenceTypeId = NumericNodeId(46, 0)
node.TypeDefinition = NumericNodeId(68, 0)
attrs = ua.VariableAttributes()
attrs.DisplayName = LocalizedText("EnumValues")
attrs.DataType = NumericNodeId(7594, 0)
value = []
extobj = ua.EnumValueType()
extobj.Value = 1
extobj.DisplayName.Text = 'Insert'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 2
extobj.DisplayName.Text = 'Replace'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 3
extobj.DisplayName.Text = 'Update'
value.append(extobj)
extobj = ua.EnumValueType()
extobj.Value = 4
extobj.DisplayName.Text = 'Remove'
value.append(extobj)
attrs.Value = ua.Variant(value, ua.VariantType.ExtensionObject)
attrs.ValueRank = 1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(11885, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(68, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(37, 0)
ref.SourceNodeId = NumericNodeId(11885, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(78, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(46, 0)
ref.SourceNodeId = NumericNodeId(11885, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(11293, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(719, 0)
node.BrowseName = QualifiedName('MonitoringFilter', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("MonitoringFilter")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(719, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(725, 0)
node.BrowseName = QualifiedName('EventFilter', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(719, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("EventFilter")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(725, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(719, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(948, 0)
node.BrowseName = QualifiedName('AggregateConfiguration', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("AggregateConfiguration")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(948, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(920, 0)
node.BrowseName = QualifiedName('HistoryEventFieldList', 0)
node.NodeClass = NodeClass.DataType
node.ParentNodeId = NumericNodeId(22, 0)
node.ReferenceTypeId = NumericNodeId(45, 0)
attrs = ua.DataTypeAttributes()
attrs.DisplayName = LocalizedText("HistoryEventFieldList")
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(45, 0)
ref.SourceNodeId = NumericNodeId(920, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(22, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(310, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(308, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(310, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(308, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(310, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7665, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(310, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12207, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12189, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(12207, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12189, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(12207, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12213, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(12207, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(306, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(304, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(306, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(304, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(306, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7662, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(306, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(314, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(312, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(314, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(312, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(314, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7668, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(314, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(434, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(432, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(434, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(432, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(434, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7782, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(434, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12900, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12890, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(12900, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12890, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(12900, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12902, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(12900, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12901, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12891, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(12901, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12891, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(12901, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12905, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(12901, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(346, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(344, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(346, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(344, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(346, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7698, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(346, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(318, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(316, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(318, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(316, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(318, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7671, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(318, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(321, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(319, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(321, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(319, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(321, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7674, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(321, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(324, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(322, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(324, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(322, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(324, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7677, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(324, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(327, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(325, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(327, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(325, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(327, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7680, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(327, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(940, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(938, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(940, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(938, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(940, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7683, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(940, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(378, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(376, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(378, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(376, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(378, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7728, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(378, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(381, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(379, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(381, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(379, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(381, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7731, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(381, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(384, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(382, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(384, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(382, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(384, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7734, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(384, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(387, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(385, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(387, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(385, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(387, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7737, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(387, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(539, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(537, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(539, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(537, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(539, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12718, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(539, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(542, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(540, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(542, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(540, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(542, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12721, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(542, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(333, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(331, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(333, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(331, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(333, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7686, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(333, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(585, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(583, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(585, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(583, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(585, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7929, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(585, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(588, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(586, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(588, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(586, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(588, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7932, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(588, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(591, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(589, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(591, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(589, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(591, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7935, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(591, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(594, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(592, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(594, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(592, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(594, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7938, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(594, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(597, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(595, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(597, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(595, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(597, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7941, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(597, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(600, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(598, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(600, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(598, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(600, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7944, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(600, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(603, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(601, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(603, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(601, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(603, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(7947, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(603, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(661, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(659, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(661, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(659, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(661, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8004, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(661, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(721, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(719, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(721, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(719, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(721, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8067, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(721, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(727, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(725, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(727, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(725, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(727, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8073, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(727, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(950, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(948, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(950, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(948, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(950, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8076, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(950, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(922, 0)
node.BrowseName = QualifiedName('Default Binary', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(920, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default Binary")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(922, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(920, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(922, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8172, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(922, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(309, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(308, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(309, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(308, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(309, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8300, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(309, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12195, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12189, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(12195, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12189, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(12195, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12201, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(12195, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(305, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(304, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(305, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(304, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(305, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8297, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(305, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(313, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(312, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(313, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(312, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(313, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8303, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(313, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(433, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(432, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(433, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(432, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(433, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8417, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(433, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12892, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12890, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(12892, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12890, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(12892, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12894, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(12892, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(12893, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12891, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(12893, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12891, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(12893, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12897, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(12893, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(345, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(344, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(345, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(344, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(345, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8333, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(345, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(317, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(316, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(317, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(316, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(317, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8306, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(317, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(320, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(319, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(320, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(319, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(320, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8309, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(320, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(323, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(322, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(323, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(322, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(323, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8312, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(323, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(326, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(325, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(326, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(325, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(326, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8315, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(326, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(939, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(938, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(939, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(938, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(939, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8318, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(939, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(377, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(376, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(377, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(376, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(377, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8363, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(377, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(380, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(379, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(380, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(379, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(380, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8366, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(380, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(383, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(382, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(383, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(382, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(383, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8369, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(383, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(386, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(385, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(386, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(385, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(386, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8372, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(386, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(538, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(537, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(538, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(537, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(538, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12712, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(538, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(541, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(540, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(541, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(540, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(541, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12715, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(541, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(332, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(331, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(332, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(331, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(332, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8321, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(332, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(584, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(583, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(584, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(583, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(584, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8564, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(584, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(587, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(586, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(587, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(586, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(587, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8567, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(587, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(590, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(589, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(590, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(589, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(590, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8570, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(590, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(593, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(592, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(593, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(592, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(593, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8573, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(593, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(596, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(595, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(596, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(595, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(596, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8576, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(596, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(599, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(598, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(599, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(598, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(599, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8579, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(599, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(602, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(601, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(602, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(601, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(602, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8582, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(602, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(660, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(659, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(660, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(659, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(660, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8639, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(660, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(720, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(719, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(720, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(719, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(720, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8702, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(720, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(726, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(725, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(726, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(725, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(726, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8708, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(726, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(949, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(948, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(949, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(948, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(949, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8711, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(949, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(921, 0)
node.BrowseName = QualifiedName('Default XML', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(920, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default XML")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(921, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(920, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(39, 0)
ref.SourceNodeId = NumericNodeId(921, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(8807, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(921, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15087, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(308, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15087, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(308, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15087, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15095, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12189, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15095, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12189, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15095, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15098, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(304, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15098, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(304, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15098, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15099, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(312, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15099, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(312, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15099, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15102, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(432, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15102, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(432, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15102, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15105, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12890, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15105, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12890, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15105, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15106, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(12891, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15106, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(12891, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15106, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15136, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(344, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15136, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(344, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15136, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15140, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(316, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15140, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(316, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15140, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15141, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(319, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15141, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(319, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15141, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15142, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(322, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15142, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(322, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15142, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15143, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(325, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15143, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(325, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15143, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15144, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(938, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15144, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(938, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15144, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15165, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(376, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15165, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(376, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15165, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15169, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(379, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15169, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(379, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15169, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15172, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(382, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15172, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(382, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15172, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15175, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(385, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15175, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(385, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15175, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15188, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(537, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15188, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(537, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15188, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15189, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(540, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15189, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(540, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15189, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15199, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(331, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15199, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(331, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15199, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15204, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(583, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15204, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(583, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15204, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15205, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(586, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15205, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(586, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15205, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15206, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(589, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15206, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(589, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15206, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15207, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(592, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15207, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(592, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15207, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15208, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(595, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15208, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(595, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15208, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15209, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(598, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15209, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(598, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15209, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15210, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(601, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15210, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(601, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15210, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15273, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(659, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15273, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(659, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15273, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15293, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(719, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15293, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(719, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15293, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15295, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(725, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15295, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(725, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15295, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15304, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(948, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15304, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(948, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15304, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = NumericNodeId(15349, 0)
node.BrowseName = QualifiedName('Default JSON', 0)
node.NodeClass = NodeClass.Object
node.ParentNodeId = NumericNodeId(920, 0)
node.ReferenceTypeId = NumericNodeId(38, 0)
node.TypeDefinition = NumericNodeId(76, 0)
attrs = ua.ObjectAttributes()
attrs.DisplayName = LocalizedText("Default JSON")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = False
ref.ReferenceTypeId = NumericNodeId(38, 0)
ref.SourceNodeId = NumericNodeId(15349, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(920, 0)
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = NumericNodeId(40, 0)
ref.SourceNodeId = NumericNodeId(15349, 0)
ref.TargetNodeClass = NodeClass.DataType
ref.TargetNodeId = NumericNodeId(76, 0)
refs.append(ref)
server.add_references(refs)
| 37.6905
| 814
| 0.705991
| 19,591
| 187,661
| 6.74621
| 0.030218
| 0.020944
| 0.057595
| 0.065448
| 0.926244
| 0.92299
| 0.922423
| 0.922044
| 0.918223
| 0.917815
| 0
| 0.043209
| 0.188276
| 187,661
| 4,978
| 815
| 37.698072
| 0.82442
| 0.00057
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| 0.884383
| 1
| 0
| 0.045845
| 0.003487
| 0
| 0
| 0
| 0
| 0
| 1
| 0.000208
| false
| 0.000208
| 0.000624
| 0
| 0.000832
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
88a9bf3a2449b7302e55a6d390570afc124818d8
| 120
|
py
|
Python
|
src/api/paths/v1/controllers/status.py
|
street-party/api
|
6ad05aa12709e8f7a7cf59f34f2e9b7b188e3486
|
[
"Apache-2.0"
] | null | null | null |
src/api/paths/v1/controllers/status.py
|
street-party/api
|
6ad05aa12709e8f7a7cf59f34f2e9b7b188e3486
|
[
"Apache-2.0"
] | null | null | null |
src/api/paths/v1/controllers/status.py
|
street-party/api
|
6ad05aa12709e8f7a7cf59f34f2e9b7b188e3486
|
[
"Apache-2.0"
] | null | null | null |
from src.api.paths.v1.service import response
def get():
return response.make(False, response=dict(ok=True)), 200
| 20
| 60
| 0.733333
| 19
| 120
| 4.631579
| 0.894737
| 0
| 0
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| 0
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| 0
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| 0
| 0.038462
| 0.133333
| 120
| 5
| 61
| 24
| 0.807692
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
c9c48915b0ec40d1161e27b9577bfc0cdea660ed
| 967
|
py
|
Python
|
Scripts/CSAN/src/gen_synthetic.py
|
iamkakadong/BrainResearch
|
7b7295756f5e5449616f9186023d75caa1e58188
|
[
"MIT"
] | null | null | null |
Scripts/CSAN/src/gen_synthetic.py
|
iamkakadong/BrainResearch
|
7b7295756f5e5449616f9186023d75caa1e58188
|
[
"MIT"
] | null | null | null |
Scripts/CSAN/src/gen_synthetic.py
|
iamkakadong/BrainResearch
|
7b7295756f5e5449616f9186023d75caa1e58188
|
[
"MIT"
] | null | null | null |
import numpy as np
def linear_gaussian(n_tr, n_te):
"""
Generate training and testing data with covariate shift from a linear model f(x) = x + 1 + epsilon,
where x_tr ~ N(0.5, 0.25) and x_te ~ N(0, 0.09), and epsilon ~ N(0, 0.01)
"""
x_tr = (np.random.randn(n_tr) + 0.5) * 0.5
x_te = (np.random.randn(n_te) + 0) * 0.3
y_tr = x_tr + 1 + np.random.randn(n_tr) * 0.1
y_te = x_te + 1 + np.random.randn(n_te) * 0.1
return x_tr, y_tr, x_te, y_te
def poly_gaussian(n_tr, n_te):
"""
Generate training and testing data with covariate shift from a non-linear model f(x) = x^3 - x + 1 + epsilon,
where x_tr ~ N(0.5, 0.25) and x_te ~ N(0, 0.09), and epsilon ~ N(0, 0.01)
"""
x_tr = (np.random.randn(n_tr) + 0.5) * 0.5
x_te = (np.random.randn(n_te) + 0) * 0.3
y_tr = x_tr ** 3 - x_tr + 1 + np.random.randn(n_tr) * 0.1
y_te = x_te ** 3 - x_te + 1 + np.random.randn(n_te) * 0.1
return x_tr, y_tr, x_te, y_te
| 40.291667
| 113
| 0.587384
| 211
| 967
| 2.50237
| 0.180095
| 0.051136
| 0.19697
| 0.212121
| 0.929924
| 0.878788
| 0.878788
| 0.878788
| 0.878788
| 0.878788
| 0
| 0.078404
| 0.24819
| 967
| 23
| 114
| 42.043478
| 0.647868
| 0.369183
| 0
| 0.461538
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.153846
| false
| 0
| 0.076923
| 0
| 0.384615
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
4e52b20b5ccdb248bd2398544d32a441bd80378f
| 4,732
|
py
|
Python
|
ban-kick.py
|
UnicorNora/Doopliss
|
cf479c098052944a59cd55be7c17cb39333c787a
|
[
"MIT"
] | null | null | null |
ban-kick.py
|
UnicorNora/Doopliss
|
cf479c098052944a59cd55be7c17cb39333c787a
|
[
"MIT"
] | null | null | null |
ban-kick.py
|
UnicorNora/Doopliss
|
cf479c098052944a59cd55be7c17cb39333c787a
|
[
"MIT"
] | null | null | null |
@client.command(pass_context=True)
async def ban(ctx, user: discord.Member, *, arg):
author = ctx.message.author
reason = arg
server = ctx.guild.name
data = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
staff_log = client.get_channel(780939099283521557)
embed = discord.Embed(
name="MEMBER_BANNED",
description="------------------------------------------------------",
color=0x00ff00)
embed.set_author(name="Member Banned:\nMember Banned Successfully")
embed.add_field(
name="Banned by: ", value="{}".format(author.mention), inline=False)
embed.add_field(
name="Banned: ", value="<@{}>".format(user.id), inline=False)
embed.add_field(
name="Reason: ",
value="{}\n------------------------------------------------------".
format(arg),
inline=False)
embed.set_footer(
text="Requested by {} \a {}".format(author, data),
icon_url=author.avatar_url)
await ctx.send(embed=embed)
embed = discord.Embed(
name="MEMBER_BANNED",
description="------------------------------------------------------",
color=0xff0000)
embed.set_author(name="Member Banned:")
embed.add_field(
name="Banned by: ", value="{}".format(author.mention), inline=False)
embed.add_field(
name="Banned: ", value="<@{}>".format(user.id), inline=False)
embed.add_field(
name="Reason: ",
value="{}\n------------------------------------------------------".
format(arg),
inline=False)
embed.set_footer(text="Banned at {}".format(data))
await staff_log.send(embed=embed)
embed = discord.Embed(
name="BANNED",
description="------------------------------------------------------",
color=0xff0000)
embed.set_author(name="Member Banned:\nYou've been Banned")
embed.add_field(
name="Banned by: ", value="{}".format(author.mention), inline=False)
embed.add_field(
name="Banned in: ", value="{}".format(server), inline=False)
embed.add_field(
name="Reason: ",
value="{}\n------------------------------------------------------".
format(arg),
inline=False)
embed.set_footer(text="Banned at {}".format(data))
await user.send(user, embed=embed)
await ctx.guild.ban(user, reason=reason)
@client.command(pass_context=True)
async def kick(ctx, user: discord.Member, *, arg):
author = ctx.message.author
reason = arg
server = ctx.guild.name
data = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
staff_log = client.get_channel(780939099283521557)
embed = discord.Embed(
name="MEMBER_KICKED",
description="------------------------------------------------------",
color=0x00ff00)
embed.set_author(name="Member Kicked:\nMember Kicked Successfully")
embed.add_field(
name="Kicked by: ", value="{}".format(author.mention), inline=False)
embed.add_field(
name="Kicked: ", value="<@{}>".format(user.id), inline=False)
embed.add_field(
name="Kicked: ",
value="{}\n------------------------------------------------------".
format(arg),
inline=False)
embed.set_footer(
text="Requested by {} \a {}".format(author, data),
icon_url=author.avatar_url)
await ctx.send(embed=embed)
embed = discord.Embed(
name="MEMBER_KICKED",
description="------------------------------------------------------",
color=0xff0000)
embed.set_author(name="Member Kicked:")
embed.add_field(
name="Kicked by: ", value="{}".format(author.mention), inline=False)
embed.add_field(
name="Kicked: ", value="<@{}>".format(user.id), inline=False)
embed.add_field(
name="Reason: ",
value="{}\n------------------------------------------------------".
format(arg),
inline=False)
embed.set_footer(text="Kicked at {}".format(data))
await staff_log.send(embed=embed)
embed = discord.Embed(
name="KICKED",
description="------------------------------------------------------",
color=0xff0000)
embed.set_author(name="Member Kicked:\nYou've been Kicked")
embed.add_field(
name="Kicked by: ", value="{}".format(author.mention), inline=False)
embed.add_field(
name="Kicked in: ", value="{}".format(server), inline=False)
embed.add_field(
name="Reason: ",
value="{}\n------------------------------------------------------".
format(arg),
inline=False)
embed.set_footer(text="Kicked at {}".format(data))
await user.send(user, embed=embed)
await ctx.guild.kick(user, reason=reason)
| 40.444444
| 77
| 0.52071
| 494
| 4,732
| 4.894737
| 0.137652
| 0.059553
| 0.096774
| 0.126551
| 0.961952
| 0.952026
| 0.947064
| 0.917287
| 0.870141
| 0.870141
| 0
| 0.017387
| 0.197802
| 4,732
| 117
| 78
| 40.444444
| 0.6196
| 0
| 0
| 0.87069
| 0
| 0
| 0.262835
| 0.141982
| 0
| 0
| 0.010142
| 0
| 0
| 1
| 0
| false
| 0.017241
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
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| null | 0
| 0
| 0
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| 0
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| 0
| 0
| 0
|
0
| 7
|
4e98c586d0bde38d7ab82c0c947c5520b7f6184d
| 12,573
|
py
|
Python
|
Tests/test_cabinet_ul_sibset.py
|
loopguard/sibset_qa
|
efd32aa0e971d715ea459e27fb72b874b9fc469a
|
[
"Apache-2.0"
] | null | null | null |
Tests/test_cabinet_ul_sibset.py
|
loopguard/sibset_qa
|
efd32aa0e971d715ea459e27fb72b874b9fc469a
|
[
"Apache-2.0"
] | null | null | null |
Tests/test_cabinet_ul_sibset.py
|
loopguard/sibset_qa
|
efd32aa0e971d715ea459e27fb72b874b9fc469a
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Вход в личный кабинет Юр.лиц по адресу http://cabinet-ul.sibset.ru/login
Проверка входа с корректными данными и выход из личного кабинета
Проба работы с Unittest вместо pytest
Chromedriver
"""
from selenium import webdriver
import unittest, time
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.chrome.options import Options
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver.common.by import By
class login_ul_chrome_test(unittest.TestCase):
"""
Тесты под управлением драйвера Chrome
"""
def setUp(self):
"""
Раскоментить опции для запуска браузера Хром без отрисовки UI в режиме --headless
"""
#options.binary_location = '/Users/a.efimov/AppData/Local/Google/Chrome SxS/Application/chrome.exe'
#options.add_argument('headless')
options = webdriver.ChromeOptions()
self.driver = webdriver.Chrome(chrome_options=options)
self.driver.get('http://cabinet-ul.sibset.ru/login')
def test_1_login(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Вводим корректные данные для авторизации
3.Проверяем что мы в ЛК
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="root"]/div/div/div/div[4]/div/div[1]/div[2]/a[1]')))
def test_2_logout(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку Выход, жмем
3.Проверяем что мы вышли из ЛК
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="root"]/div/div/div/header/div[2]/a[2]'))) #Выход
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/header/div[2]/a[2]').click()
def test_3_incorrectinput(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Вводим некорректные данные авторизации
3.Проверяем наличие 'error' оповещения о неверном вводе данных
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('1234')
driver.find_element_by_name('password').send_keys('#$^')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'error')))
def test_4_settings(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку перехода к настройкам, жмем
3.Проверяем что мы в разделе настроек
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_css_selector('#root > div > div > div > header > div.additional_menu > a:nth-child(1)').click()
wait.until(EC.presence_of_element_located((By.NAME, 'old_password')))
def test_5_docs(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку перехода к документам, жмем
3.Проверяем что мы в разделе документы
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[1]/a[2]').click()
wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'title')))
def test_6_notification(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку перехода к уведомлениям, жмем
3.Проверяем что мы в разделе настроек уведомлений
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[1]/a[3]/span').click()
wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="root"]/div/div/div/div[4]/div/div[1]')))
def test_7_promise_payment(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Выбираем Обещаный платеж
3.Проверяем переход на новую вкладку с оплатой
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[4]/div/div[1]/div[2]/a[1]').click()
driver.get('http://insufficient-funds.211.ru/?account=1000000212140')
wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'send_promise')))
def test_8_ul_payment(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Выбираем Способы Оплаты
3.Проверяем переход на новую вкладку с оплатой для юр лиц
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[4]/div/div[1]/div[2]/a[2]').click()
driver.get('http://nsk.sibset.ru/b2b/abonentam/?account=1000000212140')
wait.until(EC.presence_of_element_located((By.ID, "account")))
def tearDown(self):
self.driver.close()
class login_ul_firefox_test(unittest.TestCase):
"""
Тесты под управлением драйвера Firefox(Gekodriver)
"""
def setUp(self):
self.driver = webdriver.Firefox()
self.driver.get('http://cabinet-ul.sibset.ru/login')
def test_1_login(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Вводим корректные данные для авторизации
3.Проверяем что мы в ЛК
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="root"]/div/div/div/div[4]/div/div[1]/div[2]/a[1]')))
def test_2_logout(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку Выход, жмем
3.Проверяем что мы вышли из ЛК
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="root"]/div/div/div/header/div[2]/a[2]'))) #Выход
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/header/div[2]/a[2]').click()
def test_3_incorrectinput(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Вводим некорректные данные авторизации
3.Проверяем наличие 'error' оповещения о неверном вводе данных
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('1234')
driver.find_element_by_name('password').send_keys('#$^')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'error')))
def test_4_settings(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку перехода к настройкам, жмем
3.Проверяем что мы в разделе настроек
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_css_selector('#root > div > div > div > header > div.additional_menu > a:nth-child(1)').click()
wait.until(EC.presence_of_element_located((By.NAME, 'old_password')))
def test_5_docs(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку перехода к документам, жмем
3.Проверяем что мы в разделе документы
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[1]/a[2]').click()
wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'title')))
def test_6_notification(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Находим кнопку перехода к уведомлениям, жмем
3.Проверяем что мы в разделе настроек уведомлений
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[1]/a[3]/span').click()
wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="root"]/div/div/div/div[4]/div/div[1]')))
def test_7_promise_payment(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Выбираем Обещаный платеж
3.Проверяем переход на новую вкладку с оплатой
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[4]/div/div[1]/div[2]/a[1]').click()
driver.get('http://insufficient-funds.211.ru/?account=1000000212140')
wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'send_promise')))
def test_8_ul_payment(self):
"""
1.Заходим на страницу личного кабинета Юр.лиц
2.Выбираем Способы Оплаты
3.Проверяем переход на новую вкладку с оплатой для юр лиц
"""
driver = self.driver
driver.find_element_by_name('login').send_keys('212140Tk')
driver.find_element_by_name('password').send_keys('212140Tk')
driver.find_element_by_class_name('ss-button').click()
wait = WebDriverWait(driver, 10)
driver.implicitly_wait(10)
driver.find_element_by_xpath('//*[@id="root"]/div/div/div/div[4]/div/div[1]/div[2]/a[2]').click()
driver.get('http://nsk.sibset.ru/b2b/abonentam/?account=1000000212140')
wait.until(EC.presence_of_element_located((By.ID, "account")))
def tearDown(self):
self.driver.close()
if __name__ == "__main__":
unittest.main()
| 40.821429
| 126
| 0.660383
| 1,685
| 12,573
| 4.717507
| 0.115727
| 0.075481
| 0.128318
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| 0.895962
| 0.895962
| 0.892691
| 0.880866
| 0.880866
| 0.880866
| 0
| 0.039936
| 0.205361
| 12,573
| 308
| 127
| 40.821429
| 0.75568
| 0.20552
| 0
| 0.9125
| 0
| 0.0875
| 0.203656
| 0.084086
| 0
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| 1
| 0.125
| false
| 0.1125
| 0.04375
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| 0.18125
| 0
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| null | 0
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| 1
| 1
| 1
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| 1
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| null | 0
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| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
4edd452944dda85cd4a351812ffd0f6ee8766dce
| 591
|
py
|
Python
|
admin_system/boards/views.py
|
chellycheng/laotu_admin_web_system
|
3ad07431c090c335203498d1bd2f01311d0ea714
|
[
"MIT"
] | null | null | null |
admin_system/boards/views.py
|
chellycheng/laotu_admin_web_system
|
3ad07431c090c335203498d1bd2f01311d0ea714
|
[
"MIT"
] | 4
|
2021-04-08T19:39:49.000Z
|
2021-09-22T19:33:47.000Z
|
admin_system/boards/views.py
|
chellycheng/laotu_admin_web_system
|
3ad07431c090c335203498d1bd2f01311d0ea714
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render, get_object_or_404,redirect
from django.http import HttpResponse, Http404
from .models import Board, Topic, Post
from django.contrib.auth.models import User
from .forms import NewTopicForm
# Create your views here.
def home(request):
return render(request, 'login.html')
def index(request):
return render(request, 'index.html')
def forgot_password(request):
return render(request, 'forgot-password.html')
def register(request):
return render(request, 'register.html')
def register(request):
return render(request, 'register.html')
| 28.142857
| 63
| 0.766497
| 79
| 591
| 5.683544
| 0.455696
| 0.144766
| 0.211581
| 0.289532
| 0.227171
| 0.227171
| 0.227171
| 0.227171
| 0.227171
| 0
| 0
| 0.011696
| 0.13198
| 591
| 21
| 64
| 28.142857
| 0.863548
| 0.038917
| 0
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| 0
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| 0.333333
| false
| 0.133333
| 0.333333
| 0.333333
| 1
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| null | 0
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| null | 0
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| 1
| 1
| 1
| 1
| 0
|
0
| 7
|
09262c716dcf8084cc93b0273e08999be90f75d5
| 176
|
py
|
Python
|
bobstack/siptransport/tcpSIPTwistedClientProtocolFactory.py
|
bobjects/BobStack
|
c177b286075044832f44baf9ace201780c8b4320
|
[
"Apache-2.0"
] | null | null | null |
bobstack/siptransport/tcpSIPTwistedClientProtocolFactory.py
|
bobjects/BobStack
|
c177b286075044832f44baf9ace201780c8b4320
|
[
"Apache-2.0"
] | null | null | null |
bobstack/siptransport/tcpSIPTwistedClientProtocolFactory.py
|
bobjects/BobStack
|
c177b286075044832f44baf9ace201780c8b4320
|
[
"Apache-2.0"
] | null | null | null |
from twisted.internet import protocol
from tcpSIPTwistedClientProtocol import TCPSIPTwistedClientProtcol
class TCPSIPTwistedClientProtocolFactory(protocol.Factory):
pass
| 25.142857
| 66
| 0.875
| 14
| 176
| 11
| 0.785714
| 0
| 0
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| 0.096591
| 176
| 6
| 67
| 29.333333
| 0.968553
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| true
| 0.25
| 0.5
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| 1
| null | 0
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 7
|
093ca3231e8425e6b91a0659d3886343a83d92c5
| 3,372
|
py
|
Python
|
authors/apps/authentication/tests/test_notifications.py
|
andela/ah-backend-thanos
|
baf7f20a023cc3c3ecae0fcf91bb7d9165e79fc8
|
[
"BSD-3-Clause"
] | null | null | null |
authors/apps/authentication/tests/test_notifications.py
|
andela/ah-backend-thanos
|
baf7f20a023cc3c3ecae0fcf91bb7d9165e79fc8
|
[
"BSD-3-Clause"
] | 42
|
2018-10-24T08:21:07.000Z
|
2021-06-10T20:54:39.000Z
|
authors/apps/authentication/tests/test_notifications.py
|
andela/ah-backend-thanos
|
baf7f20a023cc3c3ecae0fcf91bb7d9165e79fc8
|
[
"BSD-3-Clause"
] | 2
|
2018-11-05T08:56:42.000Z
|
2019-05-03T12:40:43.000Z
|
from .basetest import BaseTestCase
from rest_framework import status
from django.contrib.auth import get_user_model
from rest_framework.reverse import reverse
from django.utils.encoding import force_text
from django.utils.http import urlsafe_base64_encode
from django.contrib.auth.tokens import default_token_generator
User = get_user_model()
login_url = reverse("authentication:login")
class NotificationsTestCase(BaseTestCase):
def test_update_subscription_status(self):
create_user = User(username="danielnuwa",
email="danielnuwa@test.com"
)
create_user.set_password("testpassword#123")
create_user.save()
login_data = {"user": {"email": "danielnuwa@test.com",
"password": "testpassword#123"}}
user = User.objects.get(email=login_data["user"]["email"])
uid = force_text(urlsafe_base64_encode(user.email.encode("utf8")))
activation_token = default_token_generator.make_token(user)
url = reverse("authentication:activate_account",
args=(uid, activation_token,))
self.client.get(url, format="json")
user_id = User.objects.filter(email="danielnuwa@test.com")
for user in user_id:
user.id
login_resp = self.client.post(
login_url, login_data, format="json")
login_token = login_resp.data['token']
self.client.credentials(HTTP_AUTHORIZATION='Token ' + login_token)
subscription_status_update_url = "/api/users/{}/unsubscribe"\
.format(user.id)
response = self.client.put(subscription_status_update_url,
self.subscribe_status_update_data,
format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_update_subscription_status_invalid_user(self):
create_user = User(username="danielnuwa",
email="danielnuwa@test.com"
)
create_user.set_password("testpassword#123")
create_user.save()
login_data = {"user": {"email": "danielnuwa@test.com",
"password": "testpassword#123"}}
user = User.objects.get(email=login_data["user"]["email"])
uid = force_text(urlsafe_base64_encode(user.email.encode("utf8")))
activation_token = default_token_generator.make_token(user)
url = reverse("authentication:activate_account",
args=(uid, activation_token,))
self.client.get(url, format="json")
user_id = User.objects.filter(email="danielnuwa@test.com")
for user in user_id:
user.id
login_resp = self.client.post(
login_url, login_data, format="json")
login_token = login_resp.data['token']
self.client.credentials(HTTP_AUTHORIZATION='Token ' + login_token)
subscription_status_update_url = "/api/users/{}/unsubscribe"\
.format(40)
response = self.client.put(subscription_status_update_url,
self.subscribe_status_update_data,
format='json')
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
| 48.171429
| 74
| 0.6207
| 362
| 3,372
| 5.527624
| 0.220994
| 0.03998
| 0.056972
| 0.065967
| 0.802599
| 0.771614
| 0.771614
| 0.771614
| 0.771614
| 0.771614
| 0
| 0.01148
| 0.27669
| 3,372
| 69
| 75
| 48.869565
| 0.808938
| 0
| 0
| 0.71875
| 0
| 0
| 0.1293
| 0.033215
| 0
| 0
| 0
| 0
| 0.03125
| 1
| 0.03125
| false
| 0.0625
| 0.109375
| 0
| 0.15625
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
1197b6f2eb1f85a7d5939ce67a0f8253cb7f88d8
| 28,639
|
py
|
Python
|
rlcard/agents/cfr_all.py
|
aditya140/rlcard
|
de203b9b74a653019452aeb0622345f33dd42eda
|
[
"MIT"
] | null | null | null |
rlcard/agents/cfr_all.py
|
aditya140/rlcard
|
de203b9b74a653019452aeb0622345f33dd42eda
|
[
"MIT"
] | null | null | null |
rlcard/agents/cfr_all.py
|
aditya140/rlcard
|
de203b9b74a653019452aeb0622345f33dd42eda
|
[
"MIT"
] | null | null | null |
from pokertrees import *
from pokerstrategy import *
import random
class CounterfactualRegretMinimizer(object):
def __init__(self, rules):
self.rules = rules
self.profile = StrategyProfile(
rules, [Strategy(i) for i in range(rules.players)]
)
self.current_profile = StrategyProfile(
rules, [Strategy(i) for i in range(rules.players)]
)
self.iterations = 0
self.counterfactual_regret = []
self.action_reachprobs = []
self.tree = PublicTree(rules)
self.tree.build()
print "Information sets: {0}".format(len(self.tree.information_sets))
for s in self.profile.strategies:
s.build_default(self.tree)
self.counterfactual_regret.append(
{infoset: [0, 0, 0] for infoset in s.policy}
)
self.action_reachprobs.append({infoset: [0, 0, 0] for infoset in s.policy})
def run(self, num_iterations):
for iteration in range(num_iterations):
self.cfr()
self.iterations += 1
def cfr(self):
self.cfr_helper(self.tree.root, [{(): 1} for _ in range(self.rules.players)])
def cfr_helper(self, root, reachprobs):
if type(root) is TerminalNode:
return self.cfr_terminal_node(root, reachprobs)
if type(root) is HolecardChanceNode:
return self.cfr_holecard_node(root, reachprobs)
if type(root) is BoardcardChanceNode:
return self.cfr_boardcard_node(root, reachprobs)
return self.cfr_action_node(root, reachprobs)
def cfr_terminal_node(self, root, reachprobs):
payoffs = [None for _ in range(self.rules.players)]
for player in range(self.rules.players):
player_payoffs = {hc: 0 for hc in root.holecards[player]}
counts = {hc: 0 for hc in root.holecards[player]}
for hands, winnings in root.payoffs.items():
prob = 1.0
player_hc = None
for opp, hc in enumerate(hands):
if opp == player:
player_hc = hc
else:
prob *= reachprobs[opp][hc]
player_payoffs[player_hc] += prob * winnings[player]
counts[player_hc] += 1
for hc, count in counts.items():
if count > 0:
player_payoffs[hc] /= float(count)
payoffs[player] = player_payoffs
return payoffs
def cfr_holecard_node(self, root, reachprobs):
assert len(root.children) == 1
prevlen = len(reachprobs[0].keys()[0])
possible_deals = float(choose(len(root.deck) - prevlen, root.todeal))
next_reachprobs = [
{
hc: reachprobs[player][hc[0:prevlen]] / possible_deals
for hc in root.children[0].holecards[player]
}
for player in range(self.rules.players)
]
subpayoffs = self.cfr_helper(root.children[0], next_reachprobs)
payoffs = [
{hc: 0 for hc in root.holecards[player]}
for player in range(self.rules.players)
]
for player, subpayoff in enumerate(subpayoffs):
for hand, winnings in subpayoff.items():
hc = hand[0:prevlen]
payoffs[player][hc] += winnings
return payoffs
def cfr_boardcard_node(self, root, reachprobs):
prevlen = len(reachprobs[0].keys()[0])
possible_deals = float(choose(len(root.deck) - prevlen, root.todeal))
payoffs = [
{hc: 0 for hc in root.holecards[player]}
for player in range(self.rules.players)
]
for bc in root.children:
next_reachprobs = [
{
hc: reachprobs[player][hc] / possible_deals
for hc in bc.holecards[player]
}
for player in range(self.rules.players)
]
subpayoffs = self.cfr_helper(bc, next_reachprobs)
for player, subpayoff in enumerate(subpayoffs):
for hand, winnings in subpayoff.items():
payoffs[player][hand] += winnings
return payoffs
def cfr_action_node(self, root, reachprobs):
# Calculate strategy from counterfactual regret
strategy = self.cfr_strategy_update(root, reachprobs)
next_reachprobs = deepcopy(reachprobs)
action_probs = {
hc: strategy.probs(
self.rules.infoset_format(root.player, hc, root.board, root.bet_history)
)
for hc in reachprobs[root.player]
}
action_payoffs = [None, None, None]
if root.fold_action:
next_reachprobs[root.player] = {
hc: action_probs[hc][FOLD] * reachprobs[root.player][hc]
for hc in reachprobs[root.player]
}
action_payoffs[FOLD] = self.cfr_helper(root.fold_action, next_reachprobs)
if root.call_action:
next_reachprobs[root.player] = {
hc: action_probs[hc][CALL] * reachprobs[root.player][hc]
for hc in reachprobs[root.player]
}
action_payoffs[CALL] = self.cfr_helper(root.call_action, next_reachprobs)
if root.raise_action:
next_reachprobs[root.player] = {
hc: action_probs[hc][RAISE] * reachprobs[root.player][hc]
for hc in reachprobs[root.player]
}
action_payoffs[RAISE] = self.cfr_helper(root.raise_action, next_reachprobs)
payoffs = []
for player in range(self.rules.players):
player_payoffs = {hc: 0 for hc in reachprobs[player]}
for i, subpayoff in enumerate(action_payoffs):
if subpayoff is None:
continue
for hc, winnings in subpayoff[player].iteritems():
# action_probs is baked into reachprobs for everyone except the acting player
if player == root.player:
player_payoffs[hc] += winnings * action_probs[hc][i]
else:
player_payoffs[hc] += winnings
payoffs.append(player_payoffs)
# Update regret calculations
self.cfr_regret_update(root, action_payoffs, payoffs[root.player])
return payoffs
def cfr_strategy_update(self, root, reachprobs):
if self.iterations == 0:
default_strat = self.profile.strategies[root.player]
for hc in root.holecards[root.player]:
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
probs = default_strat.probs(infoset)
for i in range(3):
self.action_reachprobs[root.player][infoset][i] += (
reachprobs[root.player][hc] * probs[i]
)
return default_strat
for hc in root.holecards[root.player]:
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
prev_cfr = self.counterfactual_regret[root.player][infoset]
sumpos_cfr = sum([max(0, x) for x in prev_cfr])
if sumpos_cfr == 0:
probs = self.equal_probs(root)
else:
probs = [max(0, x) / sumpos_cfr for x in prev_cfr]
self.current_profile.strategies[root.player].policy[infoset] = probs
for i in range(3):
self.action_reachprobs[root.player][infoset][i] += (
reachprobs[root.player][hc] * probs[i]
)
self.profile.strategies[root.player].policy[infoset] = [
self.action_reachprobs[root.player][infoset][i]
/ sum(self.action_reachprobs[root.player][infoset])
for i in range(3)
]
return self.current_profile.strategies[root.player]
def cfr_regret_update(self, root, action_payoffs, ev):
for i, subpayoff in enumerate(action_payoffs):
if subpayoff is None:
continue
for hc, winnings in subpayoff[root.player].iteritems():
immediate_cfr = winnings - ev[hc]
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
self.counterfactual_regret[root.player][infoset][i] += immediate_cfr
def equal_probs(self, root):
total_actions = len(root.children)
probs = [0, 0, 0]
if root.fold_action:
probs[FOLD] = 1.0 / total_actions
if root.call_action:
probs[CALL] = 1.0 / total_actions
if root.raise_action:
probs[RAISE] = 1.0 / total_actions
return probs
class PublicChanceSamplingCFR(CounterfactualRegretMinimizer):
def __init__(self, rules):
CounterfactualRegretMinimizer.__init__(self, rules)
def cfr(self):
# Sample all board cards to be used
self.board = random.sample(
self.rules.deck, sum([x.boardcards for x in self.rules.roundinfo])
)
# Call the standard CFR algorithm
self.cfr_helper(self.tree.root, [{(): 1} for _ in range(self.rules.players)])
def cfr_terminal_node(self, root, reachprobs):
payoffs = [None for _ in range(self.rules.players)]
for player in range(self.rules.players):
player_payoffs = {hc: 0 for hc in reachprobs[player]}
counts = {hc: 0 for hc in reachprobs[player]}
for hands, winnings in root.payoffs.items():
if not self.terminal_match(hands):
continue
prob = 1.0
player_hc = None
for opp, hc in enumerate(hands):
if opp == player:
player_hc = hc
else:
prob *= reachprobs[opp][hc]
player_payoffs[player_hc] += prob * winnings[player]
counts[player_hc] += 1
for hc, count in counts.items():
if count > 0:
player_payoffs[hc] /= float(count)
payoffs[player] = player_payoffs
return payoffs
def terminal_match(self, hands):
for hc in hands:
if self.has_boardcard(hc):
return False
return True
def cfr_holecard_node(self, root, reachprobs):
assert len(root.children) == 1
prevlen = len(reachprobs[0].keys()[0])
possible_deals = float(
choose(len(root.deck) - len(self.board) - prevlen, root.todeal)
)
next_reachprobs = [
{
hc: reachprobs[player][hc[0:prevlen]] / possible_deals
for hc in root.children[0].holecards[player]
if not self.has_boardcard(hc)
}
for player in range(self.rules.players)
]
subpayoffs = self.cfr_helper(root.children[0], next_reachprobs)
payoffs = [
{hc: 0 for hc in reachprobs[player]} for player in range(self.rules.players)
]
for player, subpayoff in enumerate(subpayoffs):
for hand, winnings in subpayoff.items():
hc = hand[0:prevlen]
payoffs[player][hc] += winnings
return payoffs
def has_boardcard(self, hc):
for c in hc:
if c in self.board:
return True
return False
def cfr_boardcard_node(self, root, reachprobs):
# Number of community cards dealt this round
num_dealt = len(root.children[0].board) - len(root.board)
# Find the child that matches the sampled board card(s)
for bc in root.children:
if self.boardmatch(num_dealt, bc):
# Update the probabilities for each HC. Assume chance prob = 1 and renormalize reach probs by new holecard range
# next_reachprobs = [{ hc: reachprobs[player][hc] for hc in reachprobs[player] } for player in range(self.rules.players)]
# sumprobs = [sum(next_reachprobs[player].values()) for player in range(self.rules.players)]
# if min(sumprobs) == 0:
# return [{ hc: 0 for hc in reachprobs[player] } for player in range(self.rules.players)]
# next_reachprobs = [{ hc: reachprobs[player][hc] / sumprobs[player] for hc in bc.holecards[player] } for player in range(self.rules.players)]
# Perform normal CFR
results = self.cfr_helper(bc, reachprobs)
# Return the payoffs
return results
raise Exception("Sampling from impossible board card")
def boardmatch(self, num_dealt, node):
# Checks if this node is a match for the sampled board card(s)
for next_card in range(0, len(node.board)):
if self.board[next_card] not in node.board:
return False
return True
def cfr_strategy_update(self, root, reachprobs):
# Update the strategies and regrets for each infoset
for hc in reachprobs[root.player]:
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
# Get the current CFR
prev_cfr = self.counterfactual_regret[root.player][infoset]
# Get the total positive CFR
sumpos_cfr = float(sum([max(0, x) for x in prev_cfr]))
if sumpos_cfr == 0:
# Default strategy is equal probability
probs = self.equal_probs(root)
else:
# Use the strategy that's proportional to accumulated positive CFR
probs = [max(0, x) / sumpos_cfr for x in prev_cfr]
# Use the updated strategy as our current strategy
self.current_profile.strategies[root.player].policy[infoset] = probs
# Update the weighted policy probabilities (used to recover the average strategy)
for i in range(3):
self.action_reachprobs[root.player][infoset][i] += (
reachprobs[root.player][hc] * probs[i]
)
if sum(self.action_reachprobs[root.player][infoset]) == 0:
# Default strategy is equal weight
self.profile.strategies[root.player].policy[infoset] = self.equal_probs(
root
)
else:
# Recover the weighted average strategy
self.profile.strategies[root.player].policy[infoset] = [
self.action_reachprobs[root.player][infoset][i]
/ sum(self.action_reachprobs[root.player][infoset])
for i in range(3)
]
# Return and use the current CFR strategy
return self.current_profile.strategies[root.player]
class ChanceSamplingCFR(CounterfactualRegretMinimizer):
def __init__(self, rules):
CounterfactualRegretMinimizer.__init__(self, rules)
def cfr(self):
# Sample all cards to be used
holecards_per_player = sum([x.holecards for x in self.rules.roundinfo])
boardcards_per_hand = sum([x.boardcards for x in self.rules.roundinfo])
todeal = random.sample(
self.rules.deck,
boardcards_per_hand + holecards_per_player * self.rules.players,
)
# Deal holecards
self.holecards = [
tuple(todeal[p * holecards_per_player : (p + 1) * holecards_per_player])
for p in range(self.rules.players)
]
self.board = tuple(todeal[-boardcards_per_hand:])
# Set the top card of the deck
self.top_card = len(todeal) - boardcards_per_hand
# Call the standard CFR algorithm
self.cfr_helper(self.tree.root, [1 for _ in range(self.rules.players)])
def cfr_terminal_node(self, root, reachprobs):
payoffs = [0 for _ in range(self.rules.players)]
for hands, winnings in root.payoffs.items():
if not self.terminal_match(hands):
continue
for player in range(self.rules.players):
prob = 1.0
for opp, hc in enumerate(hands):
if opp != player:
prob *= reachprobs[opp]
payoffs[player] = prob * winnings[player]
return payoffs
def terminal_match(self, hands):
for p in range(self.rules.players):
if not self.hcmatch(hands[p], p):
return False
return True
def hcmatch(self, hc, player):
# Checks if this hand is isomorphic to the sampled hand
sampled = self.holecards[player][: len(hc)]
for c in hc:
if c not in sampled:
return False
return True
def cfr_holecard_node(self, root, reachprobs):
assert len(root.children) == 1
return self.cfr_helper(root.children[0], reachprobs)
def cfr_boardcard_node(self, root, reachprobs):
# Number of community cards dealt this round
num_dealt = len(root.children[0].board) - len(root.board)
# Find the child that matches the sampled board card(s)
for bc in root.children:
if self.boardmatch(num_dealt, bc):
# Perform normal CFR
results = self.cfr_helper(bc, reachprobs)
# Return the payoffs
return results
raise Exception("Sampling from impossible board card")
def boardmatch(self, num_dealt, node):
# Checks if this node is a match for the sampled board card(s)
for next_card in range(0, len(node.board)):
if self.board[next_card] not in node.board:
return False
return True
def cfr_action_node(self, root, reachprobs):
# Calculate strategy from counterfactual regret
strategy = self.cfr_strategy_update(root, reachprobs)
next_reachprobs = deepcopy(reachprobs)
hc = self.holecards[root.player][0 : len(root.holecards[root.player])]
action_probs = strategy.probs(
self.rules.infoset_format(root.player, hc, root.board, root.bet_history)
)
action_payoffs = [None, None, None]
if root.fold_action:
next_reachprobs[root.player] = action_probs[FOLD] * reachprobs[root.player]
action_payoffs[FOLD] = self.cfr_helper(root.fold_action, next_reachprobs)
if root.call_action:
next_reachprobs[root.player] = action_probs[CALL] * reachprobs[root.player]
action_payoffs[CALL] = self.cfr_helper(root.call_action, next_reachprobs)
if root.raise_action:
next_reachprobs[root.player] = action_probs[RAISE] * reachprobs[root.player]
action_payoffs[RAISE] = self.cfr_helper(root.raise_action, next_reachprobs)
payoffs = [0 for player in range(self.rules.players)]
for i, subpayoff in enumerate(action_payoffs):
if subpayoff is None:
continue
for player, winnings in enumerate(subpayoff):
# action_probs is baked into reachprobs for everyone except the acting player
if player == root.player:
payoffs[player] += winnings * action_probs[i]
else:
payoffs[player] += winnings
# Update regret calculations
self.cfr_regret_update(root, action_payoffs, payoffs[root.player])
return payoffs
def cfr_strategy_update(self, root, reachprobs):
# Update the strategies and regrets for each infoset
hc = self.holecards[root.player][0 : len(root.holecards[root.player])]
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
# Get the current CFR
prev_cfr = self.counterfactual_regret[root.player][infoset]
# Get the total positive CFR
sumpos_cfr = float(sum([max(0, x) for x in prev_cfr]))
if sumpos_cfr == 0:
# Default strategy is equal probability
probs = self.equal_probs(root)
else:
# Use the strategy that's proportional to accumulated positive CFR
probs = [max(0, x) / sumpos_cfr for x in prev_cfr]
# Use the updated strategy as our current strategy
self.current_profile.strategies[root.player].policy[infoset] = probs
# Update the weighted policy probabilities (used to recover the average strategy)
for i in range(3):
self.action_reachprobs[root.player][infoset][i] += (
reachprobs[root.player] * probs[i]
)
if sum(self.action_reachprobs[root.player][infoset]) == 0:
# Default strategy is equal weight
self.profile.strategies[root.player].policy[infoset] = self.equal_probs(
root
)
else:
# Recover the weighted average strategy
self.profile.strategies[root.player].policy[infoset] = [
self.action_reachprobs[root.player][infoset][i]
/ sum(self.action_reachprobs[root.player][infoset])
for i in range(3)
]
# Return and use the current CFR strategy
return self.current_profile.strategies[root.player]
def cfr_regret_update(self, root, action_payoffs, ev):
hc = self.holecards[root.player][0 : len(root.holecards[root.player])]
for i, subpayoff in enumerate(action_payoffs):
if subpayoff is None:
continue
immediate_cfr = subpayoff[root.player] - ev
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
self.counterfactual_regret[root.player][infoset][i] += immediate_cfr
class OutcomeSamplingCFR(ChanceSamplingCFR):
def __init__(self, rules, exploration=0.4):
ChanceSamplingCFR.__init__(self, rules)
self.exploration = exploration
def cfr(self):
# Sample all cards to be used
holecards_per_player = sum([x.holecards for x in self.rules.roundinfo])
boardcards_per_hand = sum([x.boardcards for x in self.rules.roundinfo])
todeal = random.sample(
self.rules.deck,
boardcards_per_hand + holecards_per_player * self.rules.players,
)
# Deal holecards
self.holecards = [
tuple(todeal[p * holecards_per_player : (p + 1) * holecards_per_player])
for p in range(self.rules.players)
]
self.board = tuple(todeal[-boardcards_per_hand:])
# Set the top card of the deck
self.top_card = len(todeal) - boardcards_per_hand
# Call the standard CFR algorithm
self.cfr_helper(self.tree.root, [1 for _ in range(self.rules.players)], 1.0)
def cfr_helper(self, root, reachprobs, sampleprobs):
if type(root) is TerminalNode:
return self.cfr_terminal_node(root, reachprobs, sampleprobs)
if type(root) is HolecardChanceNode:
return self.cfr_holecard_node(root, reachprobs, sampleprobs)
if type(root) is BoardcardChanceNode:
return self.cfr_boardcard_node(root, reachprobs, sampleprobs)
return self.cfr_action_node(root, reachprobs, sampleprobs)
def cfr_terminal_node(self, root, reachprobs, sampleprobs):
payoffs = [0 for _ in range(self.rules.players)]
for hands, winnings in root.payoffs.items():
if not self.terminal_match(hands):
continue
for player in range(self.rules.players):
prob = 1.0
for opp, hc in enumerate(hands):
if opp != player:
prob *= reachprobs[opp]
payoffs[player] = prob * winnings[player] / sampleprobs
return payoffs
def cfr_holecard_node(self, root, reachprobs, sampleprobs):
assert len(root.children) == 1
return self.cfr_helper(root.children[0], reachprobs, sampleprobs)
def cfr_boardcard_node(self, root, reachprobs, sampleprobs):
# Number of community cards dealt this round
num_dealt = len(root.children[0].board) - len(root.board)
# Find the child that matches the sampled board card(s)
for bc in root.children:
if self.boardmatch(num_dealt, bc):
# Perform normal CFR
results = self.cfr_helper(bc, reachprobs, sampleprobs)
# Return the payoffs
return results
raise Exception("Sampling from impossible board card")
def cfr_action_node(self, root, reachprobs, sampleprobs):
# Calculate strategy from counterfactual regret
strategy = self.cfr_strategy_update(root, reachprobs, sampleprobs)
hc = self.holecards[root.player][0 : len(root.holecards[root.player])]
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
action_probs = strategy.probs(infoset)
if random.random() < self.exploration:
action = self.random_action(root)
else:
action = strategy.sample_action(infoset)
reachprobs[root.player] *= action_probs[action]
csp = (
self.exploration * (1.0 / len(root.children))
+ (1.0 - self.exploration) * action_probs[action]
)
payoffs = self.cfr_helper(root.get_child(action), reachprobs, sampleprobs * csp)
# Update regret calculations
self.cfr_regret_update(root, payoffs[root.player], action, action_probs[action])
payoffs[root.player] *= action_probs[action]
return payoffs
def random_action(self, root):
options = []
if root.fold_action:
options.append(FOLD)
if root.call_action:
options.append(CALL)
if root.raise_action:
options.append(RAISE)
return random.choice(options)
def cfr_strategy_update(self, root, reachprobs, sampleprobs):
# Update the strategies and regrets for each infoset
hc = self.holecards[root.player][0 : len(root.holecards[root.player])]
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
# Get the current CFR
prev_cfr = self.counterfactual_regret[root.player][infoset]
# Get the total positive CFR
sumpos_cfr = float(sum([max(0, x) for x in prev_cfr]))
if sumpos_cfr == 0:
# Default strategy is equal probability
probs = self.equal_probs(root)
else:
# Use the strategy that's proportional to accumulated positive CFR
probs = [max(0, x) / sumpos_cfr for x in prev_cfr]
# Use the updated strategy as our current strategy
self.current_profile.strategies[root.player].policy[infoset] = probs
# Update the weighted policy probabilities (used to recover the average strategy)
for i in range(3):
self.action_reachprobs[root.player][infoset][i] += (
reachprobs[root.player] * probs[i] / sampleprobs
)
if sum(self.action_reachprobs[root.player][infoset]) == 0:
# Default strategy is equal weight
self.profile.strategies[root.player].policy[infoset] = self.equal_probs(
root
)
else:
# Recover the weighted average strategy
self.profile.strategies[root.player].policy[infoset] = [
self.action_reachprobs[root.player][infoset][i]
/ sum(self.action_reachprobs[root.player][infoset])
for i in range(3)
]
# Return and use the current CFR strategy
return self.current_profile.strategies[root.player]
def cfr_regret_update(self, root, ev, action, actionprob):
hc = self.holecards[root.player][0 : len(root.holecards[root.player])]
infoset = self.rules.infoset_format(
root.player, hc, root.board, root.bet_history
)
for i in range(3):
if not root.valid(i):
continue
immediate_cfr = -ev * actionprob
if action == i:
immediate_cfr += ev
self.counterfactual_regret[root.player][infoset][i] += immediate_cfr
| 44.195988
| 158
| 0.59178
| 3,331
| 28,639
| 4.969379
| 0.060342
| 0.057391
| 0.047121
| 0.026098
| 0.862261
| 0.847339
| 0.833021
| 0.808736
| 0.790854
| 0.773153
| 0
| 0.005885
| 0.317644
| 28,639
| 647
| 159
| 44.264297
| 0.841163
| 0.109117
| 0
| 0.662289
| 0
| 0
| 0.004952
| 0
| 0
| 0
| 0
| 0
| 0.007505
| 0
| null | null | 0
| 0.005629
| null | null | 0.001876
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
11c34559f05c0dfac23b0b52efa6b89e5fcb2678
| 577,665
|
py
|
Python
|
tests/examples/minlplib/ringpack_20_2.py
|
ouyang-w-19/decogo
|
52546480e49776251d4d27856e18a46f40c824a1
|
[
"MIT"
] | 2
|
2021-07-03T13:19:10.000Z
|
2022-02-06T10:48:13.000Z
|
tests/examples/minlplib/ringpack_20_2.py
|
ouyang-w-19/decogo
|
52546480e49776251d4d27856e18a46f40c824a1
|
[
"MIT"
] | 1
|
2021-07-04T14:52:14.000Z
|
2021-07-15T10:17:11.000Z
|
tests/examples/minlplib/ringpack_20_2.py
|
ouyang-w-19/decogo
|
52546480e49776251d4d27856e18a46f40c824a1
|
[
"MIT"
] | null | null | null |
# MINLP written by GAMS Convert at 04/21/18 13:54:02
#
# Equation counts
# Total E G L N X C B
# 2928 1 2562 365 0 0 0 0
#
# Variable counts
# x b i s1s s2s sc si
# Total cont binary integer sos1 sos2 scont sint
# 236 41 195 0 0 0 0 0
# FX 0 0 0 0 0 0 0 0
#
# Nonzero counts
# Total const NL DLL
# 17073 6205 10868 0
#
# Reformulation has removed 1 variable and 1 equation
from pyomo.environ import *
model = m = ConcreteModel()
m.b2 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b3 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b4 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b5 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b6 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b7 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b8 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b9 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b10 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b11 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b12 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b13 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b14 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b15 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b16 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b17 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b18 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b19 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b20 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b21 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b22 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b23 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b24 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b25 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b26 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b27 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b28 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b29 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b30 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b31 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b32 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b33 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b34 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b35 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b36 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b37 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b38 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b39 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b40 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b41 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b42 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b43 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b44 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b45 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b46 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b47 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b48 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b49 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b50 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b51 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b52 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b53 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b54 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b55 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b56 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b57 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b58 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b59 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b60 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b61 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b62 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b63 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b64 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b65 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b66 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b67 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b68 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b69 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b70 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b71 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b72 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b73 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b74 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b75 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b76 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b77 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b78 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b79 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b80 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b81 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b82 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b83 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b84 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b85 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b86 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b87 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b88 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b89 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b90 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b91 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b92 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b93 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b94 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b95 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b96 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b97 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b98 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b99 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b100 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b101 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b102 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b103 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b104 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b105 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b106 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b107 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b108 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b109 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b110 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b111 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b112 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b113 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b114 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b115 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b116 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b117 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b118 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b119 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b120 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b121 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b122 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b123 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b124 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b125 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b126 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b127 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b128 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b129 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b130 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b131 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b132 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b133 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b134 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b135 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b136 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b137 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b138 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b139 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b140 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b141 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b142 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b143 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b144 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b145 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b146 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b147 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b148 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b149 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b150 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b151 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b152 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b153 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b154 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b155 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b156 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b157 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b158 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b159 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b160 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b161 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b162 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b163 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b164 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b165 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b166 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b167 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b168 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b169 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b170 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b171 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b172 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b173 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b174 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b175 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b176 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b177 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b178 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b179 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b180 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b181 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b182 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b183 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b184 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b185 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b186 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b187 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b188 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b189 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b190 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b191 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b192 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b193 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b194 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b195 = Var(within=Binary,bounds=(0,1),initialize=0)
m.b196 = Var(within=Binary,bounds=(0,1),initialize=0)
m.x197 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x198 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x199 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x200 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x201 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x202 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x203 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x204 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x205 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x206 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x207 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x208 = Var(within=Reals,bounds=(0.599362,9.400638),initialize=0.599362)
m.x209 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x210 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x211 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x212 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x213 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x214 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x215 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x216 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x217 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x218 = Var(within=Reals,bounds=(0.342553,9.657447),initialize=0.342553)
m.x219 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x220 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x221 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x222 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x223 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x224 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x225 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x226 = Var(within=Reals,bounds=(0.85617,9.14383),initialize=0.85617)
m.x227 = Var(within=Reals,bounds=(1.52426,8.47574),initialize=1.52426)
m.x228 = Var(within=Reals,bounds=(1.52426,8.47574),initialize=1.52426)
m.x229 = Var(within=Reals,bounds=(1.52426,8.47574),initialize=1.52426)
m.x230 = Var(within=Reals,bounds=(1.52426,8.47574),initialize=1.52426)
m.x231 = Var(within=Reals,bounds=(1.52426,8.47574),initialize=1.52426)
m.x232 = Var(within=Reals,bounds=(1.52426,8.47574),initialize=1.52426)
m.x233 = Var(within=Reals,bounds=(1.93213,8.06787),initialize=1.93213)
m.x234 = Var(within=Reals,bounds=(1.93213,8.06787),initialize=1.93213)
m.x235 = Var(within=Reals,bounds=(1.93213,8.06787),initialize=1.93213)
m.x236 = Var(within=Reals,bounds=(1.93213,8.06787),initialize=1.93213)
m.obj = Objective(expr= - 0.287403606378025*m.b2 - 0.287403606378025*m.b3 - 0.287403606378025*m.b4
- 0.287403606378025*m.b5 - 0.287403606378025*m.b6 - 0.287403606378025*m.b7
- 0.287403606378025*m.b8 - 0.287403606378025*m.b9 - 0.287403606378025*m.b10
- 0.287403606378025*m.b11 - 0.287403606378025*m.b12 - 0.287403606378025*m.b13
- 0.0946538180425961*m.b14 - 0.0946538180425961*m.b15 - 0.0946538180425961*m.b16
- 0.0946538180425961*m.b17 - 0.0946538180425961*m.b18 - 0.0946538180425961*m.b19
- 0.0946538180425961*m.b20 - 0.0946538180425961*m.b21 - 0.0946538180425961*m.b22
- 0.0946538180425961*m.b23 - 0.584516458645496*m.b24 - 0.584516458645496*m.b25
- 0.584516458645496*m.b26 - 0.584516458645496*m.b27 - 0.584516458645496*m.b28
- 0.584516458645496*m.b29 - 0.584516458645496*m.b30 - 0.584516458645496*m.b31
- 1.29409755392315*m.b32 - 1.29409755392315*m.b33 - 1.29409755392315*m.b34
- 1.29409755392315*m.b35 - 1.29409755392315*m.b36 - 1.29409755392315*m.b37
- 2.08582176557546*m.b38 - 2.08582176557546*m.b39 - 2.08582176557546*m.b40
- 2.08582176557546*m.b41 - 2.08582176557546*m.b42 - 2.08582176557546*m.b43
- 2.08582176557546*m.b44 - 2.08582176557546*m.b45 - 2.08582176557546*m.b46
- 2.08582176557546*m.b47 - 2.08582176557546*m.b48 - 2.08582176557546*m.b49
- 2.08582176557546*m.b50 - 2.08582176557546*m.b51 - 2.08582176557546*m.b52
- 2.08582176557546*m.b53 - 2.08582176557546*m.b54 - 2.08582176557546*m.b55
- 2.08582176557546*m.b56 - 2.08582176557546*m.b57 - 2.08582176557546*m.b58
- 2.08582176557546*m.b59 - 2.08582176557546*m.b60 - 2.08582176557546*m.b61
- 2.08582176557546*m.b62 - 2.08582176557546*m.b63 - 2.08582176557546*m.b64
- 2.08582176557546*m.b65 - 2.08582176557546*m.b66 - 2.08582176557546*m.b67
- 2.08582176557546*m.b68 - 2.08582176557546*m.b69 - 2.08582176557546*m.b70
- 2.08582176557546*m.b71 - 2.08582176557546*m.b72 - 2.08582176557546*m.b73
- 2.08582176557546*m.b74 - 2.08582176557546*m.b75 - 2.08582176557546*m.b76
- 2.08582176557546*m.b77 - 2.08582176557546*m.b78 - 2.08582176557546*m.b79
- 2.08582176557546*m.b80 - 2.08582176557546*m.b81 - 2.08582176557546*m.b82
- 2.08582176557546*m.b83 - 2.08582176557546*m.b84 - 2.08582176557546*m.b85
- 2.08582176557546*m.b86 - 2.08582176557546*m.b87 - 2.08582176557546*m.b88
- 2.08582176557546*m.b89 - 2.08582176557546*m.b90 - 2.08582176557546*m.b91
- 2.08582176557546*m.b92 - 2.08582176557546*m.b93 - 2.08582176557546*m.b94
- 2.08582176557546*m.b95 - 2.08582176557546*m.b96 - 2.08582176557546*m.b97
- 2.08582176557546*m.b98 - 2.08582176557546*m.b99 - 2.08582176557546*m.b100
- 2.08582176557546*m.b101 - 2.08582176557546*m.b102 - 2.08582176557546*m.b103
- 2.08582176557546*m.b104 - 2.08582176557546*m.b105 - 2.08582176557546*m.b106
- 2.08582176557546*m.b107 - 2.08582176557546*m.b108 - 2.08582176557546*m.b109
- 2.08582176557546*m.b110 - 2.08582176557546*m.b111 - 2.08582176557546*m.b112
- 2.08582176557546*m.b113 - 2.08582176557546*m.b114 - 2.08582176557546*m.b115
- 2.08582176557546*m.b116 - 2.08582176557546*m.b117 - 2.08582176557546*m.b118
- 2.08582176557546*m.b119 - 2.08582176557546*m.b120 - 2.08582176557546*m.b121
- 2.08582176557546*m.b122 - 2.08582176557546*m.b123 - 2.08582176557546*m.b124
- 2.08582176557546*m.b125 - 2.08582176557546*m.b126 - 2.08582176557546*m.b127
- 2.08582176557546*m.b128 - 2.08582176557546*m.b129 - 2.08582176557546*m.b130
- 2.08582176557546*m.b131 - 2.08582176557546*m.b132 - 2.08582176557546*m.b133
- 2.08582176557546*m.b134 - 2.08582176557546*m.b135 - 2.08582176557546*m.b136
- 2.08582176557546*m.b137 - 2.08582176557546*m.b138 - 2.08582176557546*m.b139
- 2.08582176557546*m.b140 - 2.08582176557546*m.b141 - 2.08582176557546*m.b142
- 2.08582176557546*m.b143 - 2.08582176557546*m.b144 - 2.08582176557546*m.b145
- 2.08582176557546*m.b146 - 2.08582176557546*m.b147 - 2.08582176557546*m.b148
- 2.08582176557546*m.b149 - 2.08582176557546*m.b150 - 2.08582176557546*m.b151
- 2.08582176557546*m.b152 - 2.08582176557546*m.b153 - 2.08582176557546*m.b154
- 2.08582176557546*m.b155 - 2.08582176557546*m.b156 - 2.08582176557546*m.b157
- 2.08582176557546*m.b158 - 2.08582176557546*m.b159 - 2.08582176557546*m.b160
- 2.08582176557546*m.b161 - 2.08582176557546*m.b162 - 2.08582176557546*m.b163
- 2.08582176557546*m.b164 - 2.08582176557546*m.b165 - 2.08582176557546*m.b166
- 2.08582176557546*m.b167 - 2.08582176557546*m.b168 - 2.08582176557546*m.b169
- 2.08582176557546*m.b170 - 2.08582176557546*m.b171 - 2.08582176557546*m.b172
- 2.08582176557546*m.b173 - 2.08582176557546*m.b174 - 2.08582176557546*m.b175
- 2.08582176557546*m.b176 - 2.08582176557546*m.b177 - 2.08582176557546*m.b178
- 2.08582176557546*m.b179 - 2.08582176557546*m.b180 - 2.08582176557546*m.b181
- 2.08582176557546*m.b182 - 2.08582176557546*m.b183 - 2.08582176557546*m.b184
- 2.08582176557546*m.b185 - 2.08582176557546*m.b186 - 2.08582176557546*m.b187
- 2.08582176557546*m.b188 - 2.08582176557546*m.b189 - 2.08582176557546*m.b190
- 2.08582176557546*m.b191 - 2.08582176557546*m.b192 - 2.08582176557546*m.b193
- 2.08582176557546*m.b194 - 2.08582176557546*m.b195 - 2.08582176557546*m.b196, sense=minimize)
m.c2 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b2 - 1.436939228176*m.b4 >= -1.436939228176)
m.c3 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b3 - 1.436939228176*m.b5 >= -1.436939228176)
m.c4 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b2 - 1.436939228176*m.b6 >= -1.436939228176)
m.c5 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b3 - 1.436939228176*m.b7 >= -1.436939228176)
m.c6 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b2 - 1.436939228176*m.b8 >= -1.436939228176)
m.c7 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b3 - 1.436939228176*m.b9 >= -1.436939228176)
m.c8 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b2 - 1.436939228176*m.b10 >= -1.436939228176)
m.c9 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b3 - 1.436939228176*m.b11 >= -1.436939228176)
m.c10 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b2 - 1.436939228176*m.b12 >= -1.436939228176)
m.c11 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b3 - 1.436939228176*m.b13 >= -1.436939228176)
m.c12 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b2 - 0.887203867225*m.b14 >= -0.887203867225)
m.c13 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b3 - 0.887203867225*m.b15 >= -0.887203867225)
m.c14 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b2 - 0.887203867225*m.b16 >= -0.887203867225)
m.c15 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b3 - 0.887203867225*m.b17 >= -0.887203867225)
m.c16 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b2 - 0.887203867225*m.b18 >= -0.887203867225)
m.c17 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b3 - 0.887203867225*m.b19 >= -0.887203867225)
m.c18 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b2 - 0.887203867225*m.b20 >= -0.887203867225)
m.c19 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b3 - 0.887203867225*m.b21 >= -0.887203867225)
m.c20 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b2 - 0.887203867225*m.b22 >= -0.887203867225)
m.c21 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b3 - 0.887203867225*m.b23 >= -0.887203867225)
m.c22 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b2 - 2.118573403024*m.b24 >= -2.118573403024)
m.c23 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b3 - 2.118573403024*m.b25 >= -2.118573403024)
m.c24 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x197*m.x221 - 2*m.x198*m.x222
- 2.118573403024*m.b2 - 2.118573403024*m.b26 >= -2.118573403024)
m.c25 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x197*m.x221 - 2*m.x198*m.x222
- 2.118573403024*m.b3 - 2.118573403024*m.b27 >= -2.118573403024)
m.c26 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b2 - 2.118573403024*m.b28 >= -2.118573403024)
m.c27 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b3 - 2.118573403024*m.b29 >= -2.118573403024)
m.c28 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x197*m.x225 - 2*m.x198*m.x226
- 2.118573403024*m.b2 - 2.118573403024*m.b30 >= -2.118573403024)
m.c29 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x197*m.x225 - 2*m.x198*m.x226
- 2.118573403024*m.b3 - 2.118573403024*m.b31 >= -2.118573403024)
m.c30 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x197*m.x227 - 2*m.x198*m.x228
- 4.509770398884*m.b2 - 4.509770398884*m.b32 >= -4.509770398884)
m.c31 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x198*m.x228 - 2*m.x197*m.x227
- 4.509770398884*m.b3 - 4.509770398884*m.b33 >= -4.509770398884)
m.c32 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x198*m.x230 - 2*m.x197*m.x229
- 4.509770398884*m.b2 - 4.509770398884*m.b34 >= -4.509770398884)
m.c33 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x198*m.x230 - 2*m.x197*m.x229
- 4.509770398884*m.b3 - 4.509770398884*m.b35 >= -4.509770398884)
m.c34 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b2 - 4.509770398884*m.b36 >= -4.509770398884)
m.c35 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b3 - 4.509770398884*m.b37 >= -4.509770398884)
m.c36 = Constraint(expr=m.x197**2 + m.x198**2 + m.x233**2 + m.x234**2 - 2*m.x198*m.x234 - 2*m.x197*m.x233
- 6.408451746064*m.b2 - 6.408451746064*m.b38 >= -6.408451746064)
m.c37 = Constraint(expr=m.x197**2 + m.x198**2 + m.x233**2 + m.x234**2 - 2*m.x198*m.x234 - 2*m.x197*m.x233
- 6.408451746064*m.b3 - 6.408451746064*m.b39 >= -6.408451746064)
m.c38 = Constraint(expr=m.x197**2 + m.x198**2 + m.x235**2 + m.x236**2 - 2*m.x198*m.x236 - 2*m.x197*m.x235
- 6.408451746064*m.b2 - 6.408451746064*m.b40 >= -6.408451746064)
m.c39 = Constraint(expr=m.x197**2 + m.x198**2 + m.x235**2 + m.x236**2 - 2*m.x198*m.x236 - 2*m.x197*m.x235
- 6.408451746064*m.b3 - 6.408451746064*m.b41 >= -6.408451746064)
m.c40 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b2 - 1.436939228176*m.b4 >= -1.436939228176)
m.c41 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b3 - 1.436939228176*m.b5 >= -1.436939228176)
m.c42 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b4 - 1.436939228176*m.b6 >= -1.436939228176)
m.c43 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b5 - 1.436939228176*m.b7 >= -1.436939228176)
m.c44 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b4 - 1.436939228176*m.b8 >= -1.436939228176)
m.c45 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b5 - 1.436939228176*m.b9 >= -1.436939228176)
m.c46 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b4 - 1.436939228176*m.b10 >= -1.436939228176)
m.c47 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b5 - 1.436939228176*m.b11 >= -1.436939228176)
m.c48 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b4 - 1.436939228176*m.b12 >= -1.436939228176)
m.c49 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b5 - 1.436939228176*m.b13 >= -1.436939228176)
m.c50 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b4 - 0.887203867225*m.b14 >= -0.887203867225)
m.c51 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b5 - 0.887203867225*m.b15 >= -0.887203867225)
m.c52 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b4 - 0.887203867225*m.b16 >= -0.887203867225)
m.c53 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b5 - 0.887203867225*m.b17 >= -0.887203867225)
m.c54 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b4 - 0.887203867225*m.b18 >= -0.887203867225)
m.c55 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b5 - 0.887203867225*m.b19 >= -0.887203867225)
m.c56 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b4 - 0.887203867225*m.b20 >= -0.887203867225)
m.c57 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b5 - 0.887203867225*m.b21 >= -0.887203867225)
m.c58 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b4 - 0.887203867225*m.b22 >= -0.887203867225)
m.c59 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b5 - 0.887203867225*m.b23 >= -0.887203867225)
m.c60 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b4 - 2.118573403024*m.b24 >= -2.118573403024)
m.c61 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b5 - 2.118573403024*m.b25 >= -2.118573403024)
m.c62 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x199*m.x221 - 2*m.x200*m.x222
- 2.118573403024*m.b4 - 2.118573403024*m.b26 >= -2.118573403024)
m.c63 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b5 - 2.118573403024*m.b27 >= -2.118573403024)
m.c64 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b4 - 2.118573403024*m.b28 >= -2.118573403024)
m.c65 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b5 - 2.118573403024*m.b29 >= -2.118573403024)
m.c66 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b4 - 2.118573403024*m.b30 >= -2.118573403024)
m.c67 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b5 - 2.118573403024*m.b31 >= -2.118573403024)
m.c68 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b4 - 4.509770398884*m.b32 >= -4.509770398884)
m.c69 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b5 - 4.509770398884*m.b33 >= -4.509770398884)
m.c70 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b4 - 4.509770398884*m.b34 >= -4.509770398884)
m.c71 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b5 - 4.509770398884*m.b35 >= -4.509770398884)
m.c72 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b4 - 4.509770398884*m.b36 >= -4.509770398884)
m.c73 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b5 - 4.509770398884*m.b37 >= -4.509770398884)
m.c74 = Constraint(expr=m.x199**2 + m.x200**2 + m.x233**2 + m.x234**2 - 2*m.x199*m.x233 - 2*m.x200*m.x234
- 6.408451746064*m.b4 - 6.408451746064*m.b38 >= -6.408451746064)
m.c75 = Constraint(expr=m.x199**2 + m.x200**2 + m.x233**2 + m.x234**2 - 2*m.x199*m.x233 - 2*m.x200*m.x234
- 6.408451746064*m.b5 - 6.408451746064*m.b39 >= -6.408451746064)
m.c76 = Constraint(expr=m.x199**2 + m.x200**2 + m.x235**2 + m.x236**2 - 2*m.x200*m.x236 - 2*m.x199*m.x235
- 6.408451746064*m.b4 - 6.408451746064*m.b40 >= -6.408451746064)
m.c77 = Constraint(expr=m.x199**2 + m.x200**2 + m.x235**2 + m.x236**2 - 2*m.x200*m.x236 - 2*m.x199*m.x235
- 6.408451746064*m.b5 - 6.408451746064*m.b41 >= -6.408451746064)
m.c78 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b2 - 1.436939228176*m.b6 >= -1.436939228176)
m.c79 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b3 - 1.436939228176*m.b7 >= -1.436939228176)
m.c80 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b4 - 1.436939228176*m.b6 >= -1.436939228176)
m.c81 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b5 - 1.436939228176*m.b7 >= -1.436939228176)
m.c82 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b6 - 1.436939228176*m.b8 >= -1.436939228176)
m.c83 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b7 - 1.436939228176*m.b9 >= -1.436939228176)
m.c84 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b6 - 1.436939228176*m.b10 >= -1.436939228176)
m.c85 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b7 - 1.436939228176*m.b11 >= -1.436939228176)
m.c86 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b6 - 1.436939228176*m.b12 >= -1.436939228176)
m.c87 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b7 - 1.436939228176*m.b13 >= -1.436939228176)
m.c88 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b6 - 0.887203867225*m.b14 >= -0.887203867225)
m.c89 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b7 - 0.887203867225*m.b15 >= -0.887203867225)
m.c90 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b6 - 0.887203867225*m.b16 >= -0.887203867225)
m.c91 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b7 - 0.887203867225*m.b17 >= -0.887203867225)
m.c92 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b6 - 0.887203867225*m.b18 >= -0.887203867225)
m.c93 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b7 - 0.887203867225*m.b19 >= -0.887203867225)
m.c94 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b6 - 0.887203867225*m.b20 >= -0.887203867225)
m.c95 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b7 - 0.887203867225*m.b21 >= -0.887203867225)
m.c96 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b6 - 0.887203867225*m.b22 >= -0.887203867225)
m.c97 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b7 - 0.887203867225*m.b23 >= -0.887203867225)
m.c98 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b6 - 2.118573403024*m.b24 >= -2.118573403024)
m.c99 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b7 - 2.118573403024*m.b25 >= -2.118573403024)
m.c100 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b6 - 2.118573403024*m.b26 >= -2.118573403024)
m.c101 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b7 - 2.118573403024*m.b27 >= -2.118573403024)
m.c102 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b6 - 2.118573403024*m.b28 >= -2.118573403024)
m.c103 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x202*m.x224 - 2*m.x201*m.x223
- 2.118573403024*m.b7 - 2.118573403024*m.b29 >= -2.118573403024)
m.c104 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b6 - 2.118573403024*m.b30 >= -2.118573403024)
m.c105 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b7 - 2.118573403024*m.b31 >= -2.118573403024)
m.c106 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b6 - 4.509770398884*m.b32 >= -4.509770398884)
m.c107 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b7 - 4.509770398884*m.b33 >= -4.509770398884)
m.c108 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x201*m.x229 - 2*m.x202*m.x230
- 4.509770398884*m.b6 - 4.509770398884*m.b34 >= -4.509770398884)
m.c109 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x201*m.x229 - 2*m.x202*m.x230
- 4.509770398884*m.b7 - 4.509770398884*m.b35 >= -4.509770398884)
m.c110 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b6 - 4.509770398884*m.b36 >= -4.509770398884)
m.c111 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b7 - 4.509770398884*m.b37 >= -4.509770398884)
m.c112 = Constraint(expr=m.x201**2 + m.x202**2 + m.x233**2 + m.x234**2 - 2*m.x202*m.x234 - 2*m.x201*m.x233
- 6.408451746064*m.b6 - 6.408451746064*m.b38 >= -6.408451746064)
m.c113 = Constraint(expr=m.x201**2 + m.x202**2 + m.x233**2 + m.x234**2 - 2*m.x202*m.x234 - 2*m.x201*m.x233
- 6.408451746064*m.b7 - 6.408451746064*m.b39 >= -6.408451746064)
m.c114 = Constraint(expr=m.x201**2 + m.x202**2 + m.x235**2 + m.x236**2 - 2*m.x202*m.x236 - 2*m.x201*m.x235
- 6.408451746064*m.b6 - 6.408451746064*m.b40 >= -6.408451746064)
m.c115 = Constraint(expr=m.x201**2 + m.x202**2 + m.x235**2 + m.x236**2 - 2*m.x202*m.x236 - 2*m.x201*m.x235
- 6.408451746064*m.b7 - 6.408451746064*m.b41 >= -6.408451746064)
m.c116 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b2 - 1.436939228176*m.b8 >= -1.436939228176)
m.c117 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b3 - 1.436939228176*m.b9 >= -1.436939228176)
m.c118 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b4 - 1.436939228176*m.b8 >= -1.436939228176)
m.c119 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b5 - 1.436939228176*m.b9 >= -1.436939228176)
m.c120 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b6 - 1.436939228176*m.b8 >= -1.436939228176)
m.c121 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b7 - 1.436939228176*m.b9 >= -1.436939228176)
m.c122 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b8 - 1.436939228176*m.b10 >= -1.436939228176)
m.c123 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b9 - 1.436939228176*m.b11 >= -1.436939228176)
m.c124 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b8 - 1.436939228176*m.b12 >= -1.436939228176)
m.c125 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b9 - 1.436939228176*m.b13 >= -1.436939228176)
m.c126 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b8 - 0.887203867225*m.b14 >= -0.887203867225)
m.c127 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b9 - 0.887203867225*m.b15 >= -0.887203867225)
m.c128 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b8 - 0.887203867225*m.b16 >= -0.887203867225)
m.c129 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b9 - 0.887203867225*m.b17 >= -0.887203867225)
m.c130 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b8 - 0.887203867225*m.b18 >= -0.887203867225)
m.c131 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b9 - 0.887203867225*m.b19 >= -0.887203867225)
m.c132 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b8 - 0.887203867225*m.b20 >= -0.887203867225)
m.c133 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b9 - 0.887203867225*m.b21 >= -0.887203867225)
m.c134 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b8 - 0.887203867225*m.b22 >= -0.887203867225)
m.c135 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b9 - 0.887203867225*m.b23 >= -0.887203867225)
m.c136 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b8 - 2.118573403024*m.b24 >= -2.118573403024)
m.c137 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b9 - 2.118573403024*m.b25 >= -2.118573403024)
m.c138 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b8 - 2.118573403024*m.b26 >= -2.118573403024)
m.c139 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b9 - 2.118573403024*m.b27 >= -2.118573403024)
m.c140 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b8 - 2.118573403024*m.b28 >= -2.118573403024)
m.c141 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b9 - 2.118573403024*m.b29 >= -2.118573403024)
m.c142 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b8 - 2.118573403024*m.b30 >= -2.118573403024)
m.c143 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b9 - 2.118573403024*m.b31 >= -2.118573403024)
m.c144 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x204*m.x228 - 2*m.x203*m.x227
- 4.509770398884*m.b8 - 4.509770398884*m.b32 >= -4.509770398884)
m.c145 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x204*m.x228 - 2*m.x203*m.x227
- 4.509770398884*m.b9 - 4.509770398884*m.b33 >= -4.509770398884)
m.c146 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b8 - 4.509770398884*m.b34 >= -4.509770398884)
m.c147 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b9 - 4.509770398884*m.b35 >= -4.509770398884)
m.c148 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b8 - 4.509770398884*m.b36 >= -4.509770398884)
m.c149 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b9 - 4.509770398884*m.b37 >= -4.509770398884)
m.c150 = Constraint(expr=m.x203**2 + m.x204**2 + m.x233**2 + m.x234**2 - 2*m.x204*m.x234 - 2*m.x203*m.x233
- 6.408451746064*m.b8 - 6.408451746064*m.b38 >= -6.408451746064)
m.c151 = Constraint(expr=m.x203**2 + m.x204**2 + m.x233**2 + m.x234**2 - 2*m.x204*m.x234 - 2*m.x203*m.x233
- 6.408451746064*m.b9 - 6.408451746064*m.b39 >= -6.408451746064)
m.c152 = Constraint(expr=m.x203**2 + m.x204**2 + m.x235**2 + m.x236**2 - 2*m.x204*m.x236 - 2*m.x203*m.x235
- 6.408451746064*m.b8 - 6.408451746064*m.b40 >= -6.408451746064)
m.c153 = Constraint(expr=m.x203**2 + m.x204**2 + m.x235**2 + m.x236**2 - 2*m.x204*m.x236 - 2*m.x203*m.x235
- 6.408451746064*m.b9 - 6.408451746064*m.b41 >= -6.408451746064)
m.c154 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b2 - 1.436939228176*m.b10 >= -1.436939228176)
m.c155 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b3 - 1.436939228176*m.b11 >= -1.436939228176)
m.c156 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b4 - 1.436939228176*m.b10 >= -1.436939228176)
m.c157 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b5 - 1.436939228176*m.b11 >= -1.436939228176)
m.c158 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b6 - 1.436939228176*m.b10 >= -1.436939228176)
m.c159 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b7 - 1.436939228176*m.b11 >= -1.436939228176)
m.c160 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b8 - 1.436939228176*m.b10 >= -1.436939228176)
m.c161 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b9 - 1.436939228176*m.b11 >= -1.436939228176)
m.c162 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b10 - 1.436939228176*m.b12 >= -1.436939228176)
m.c163 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b11 - 1.436939228176*m.b13 >= -1.436939228176)
m.c164 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b10 - 0.887203867225*m.b14 >= -0.887203867225)
m.c165 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b11 - 0.887203867225*m.b15 >= -0.887203867225)
m.c166 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b10 - 0.887203867225*m.b16 >= -0.887203867225)
m.c167 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b11 - 0.887203867225*m.b17 >= -0.887203867225)
m.c168 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b10 - 0.887203867225*m.b18 >= -0.887203867225)
m.c169 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b11 - 0.887203867225*m.b19 >= -0.887203867225)
m.c170 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b10 - 0.887203867225*m.b20 >= -0.887203867225)
m.c171 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b11 - 0.887203867225*m.b21 >= -0.887203867225)
m.c172 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b10 - 0.887203867225*m.b22 >= -0.887203867225)
m.c173 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b11 - 0.887203867225*m.b23 >= -0.887203867225)
m.c174 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b10 - 2.118573403024*m.b24 >= -2.118573403024)
m.c175 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b11 - 2.118573403024*m.b25 >= -2.118573403024)
m.c176 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b10 - 2.118573403024*m.b26 >= -2.118573403024)
m.c177 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b11 - 2.118573403024*m.b27 >= -2.118573403024)
m.c178 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b10 - 2.118573403024*m.b28 >= -2.118573403024)
m.c179 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b11 - 2.118573403024*m.b29 >= -2.118573403024)
m.c180 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b10 - 2.118573403024*m.b30 >= -2.118573403024)
m.c181 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b11 - 2.118573403024*m.b31 >= -2.118573403024)
m.c182 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b10 - 4.509770398884*m.b32 >= -4.509770398884)
m.c183 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b11 - 4.509770398884*m.b33 >= -4.509770398884)
m.c184 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b10 - 4.509770398884*m.b34 >= -4.509770398884)
m.c185 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b11 - 4.509770398884*m.b35 >= -4.509770398884)
m.c186 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b10 - 4.509770398884*m.b36 >= -4.509770398884)
m.c187 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b11 - 4.509770398884*m.b37 >= -4.509770398884)
m.c188 = Constraint(expr=m.x205**2 + m.x206**2 + m.x233**2 + m.x234**2 - 2*m.x205*m.x233 - 2*m.x206*m.x234
- 6.408451746064*m.b10 - 6.408451746064*m.b38 >= -6.408451746064)
m.c189 = Constraint(expr=m.x205**2 + m.x206**2 + m.x233**2 + m.x234**2 - 2*m.x205*m.x233 - 2*m.x206*m.x234
- 6.408451746064*m.b11 - 6.408451746064*m.b39 >= -6.408451746064)
m.c190 = Constraint(expr=m.x205**2 + m.x206**2 + m.x235**2 + m.x236**2 - 2*m.x205*m.x235 - 2*m.x206*m.x236
- 6.408451746064*m.b10 - 6.408451746064*m.b40 >= -6.408451746064)
m.c191 = Constraint(expr=m.x205**2 + m.x206**2 + m.x235**2 + m.x236**2 - 2*m.x205*m.x235 - 2*m.x206*m.x236
- 6.408451746064*m.b11 - 6.408451746064*m.b41 >= -6.408451746064)
m.c192 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b2 - 1.436939228176*m.b12 >= -1.436939228176)
m.c193 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b3 - 1.436939228176*m.b13 >= -1.436939228176)
m.c194 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b4 - 1.436939228176*m.b12 >= -1.436939228176)
m.c195 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b5 - 1.436939228176*m.b13 >= -1.436939228176)
m.c196 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b6 - 1.436939228176*m.b12 >= -1.436939228176)
m.c197 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b7 - 1.436939228176*m.b13 >= -1.436939228176)
m.c198 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b8 - 1.436939228176*m.b12 >= -1.436939228176)
m.c199 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b9 - 1.436939228176*m.b13 >= -1.436939228176)
m.c200 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b10 - 1.436939228176*m.b12 >= -1.436939228176)
m.c201 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b11 - 1.436939228176*m.b13 >= -1.436939228176)
m.c202 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b12 - 0.887203867225*m.b14 >= -0.887203867225)
m.c203 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b13 - 0.887203867225*m.b15 >= -0.887203867225)
m.c204 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b12 - 0.887203867225*m.b16 >= -0.887203867225)
m.c205 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b13 - 0.887203867225*m.b17 >= -0.887203867225)
m.c206 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b12 - 0.887203867225*m.b18 >= -0.887203867225)
m.c207 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b13 - 0.887203867225*m.b19 >= -0.887203867225)
m.c208 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b12 - 0.887203867225*m.b20 >= -0.887203867225)
m.c209 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b13 - 0.887203867225*m.b21 >= -0.887203867225)
m.c210 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b12 - 0.887203867225*m.b22 >= -0.887203867225)
m.c211 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b13 - 0.887203867225*m.b23 >= -0.887203867225)
m.c212 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b12 - 2.118573403024*m.b24 >= -2.118573403024)
m.c213 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b13 - 2.118573403024*m.b25 >= -2.118573403024)
m.c214 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b12 - 2.118573403024*m.b26 >= -2.118573403024)
m.c215 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b13 - 2.118573403024*m.b27 >= -2.118573403024)
m.c216 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b12 - 2.118573403024*m.b28 >= -2.118573403024)
m.c217 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b13 - 2.118573403024*m.b29 >= -2.118573403024)
m.c218 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b12 - 2.118573403024*m.b30 >= -2.118573403024)
m.c219 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b13 - 2.118573403024*m.b31 >= -2.118573403024)
m.c220 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b12 - 4.509770398884*m.b32 >= -4.509770398884)
m.c221 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b13 - 4.509770398884*m.b33 >= -4.509770398884)
m.c222 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b12 - 4.509770398884*m.b34 >= -4.509770398884)
m.c223 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b13 - 4.509770398884*m.b35 >= -4.509770398884)
m.c224 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b12 - 4.509770398884*m.b36 >= -4.509770398884)
m.c225 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b13 - 4.509770398884*m.b37 >= -4.509770398884)
m.c226 = Constraint(expr=m.x207**2 + m.x208**2 + m.x233**2 + m.x234**2 - 2*m.x207*m.x233 - 2*m.x208*m.x234
- 6.408451746064*m.b12 - 6.408451746064*m.b38 >= -6.408451746064)
m.c227 = Constraint(expr=m.x207**2 + m.x208**2 + m.x233**2 + m.x234**2 - 2*m.x207*m.x233 - 2*m.x208*m.x234
- 6.408451746064*m.b13 - 6.408451746064*m.b39 >= -6.408451746064)
m.c228 = Constraint(expr=m.x207**2 + m.x208**2 + m.x235**2 + m.x236**2 - 2*m.x207*m.x235 - 2*m.x208*m.x236
- 6.408451746064*m.b12 - 6.408451746064*m.b40 >= -6.408451746064)
m.c229 = Constraint(expr=m.x207**2 + m.x208**2 + m.x235**2 + m.x236**2 - 2*m.x207*m.x235 - 2*m.x208*m.x236
- 6.408451746064*m.b13 - 6.408451746064*m.b41 >= -6.408451746064)
m.c230 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b2 - 0.887203867225*m.b14 >= -0.887203867225)
m.c231 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b3 - 0.887203867225*m.b15 >= -0.887203867225)
m.c232 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b4 - 0.887203867225*m.b14 >= -0.887203867225)
m.c233 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b5 - 0.887203867225*m.b15 >= -0.887203867225)
m.c234 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b6 - 0.887203867225*m.b14 >= -0.887203867225)
m.c235 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b7 - 0.887203867225*m.b15 >= -0.887203867225)
m.c236 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b8 - 0.887203867225*m.b14 >= -0.887203867225)
m.c237 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b9 - 0.887203867225*m.b15 >= -0.887203867225)
m.c238 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b10 - 0.887203867225*m.b14 >= -0.887203867225)
m.c239 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b11 - 0.887203867225*m.b15 >= -0.887203867225)
m.c240 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b12 - 0.887203867225*m.b14 >= -0.887203867225)
m.c241 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b13 - 0.887203867225*m.b15 >= -0.887203867225)
m.c242 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b14 - 0.469370231236*m.b16 >= -0.469370231236)
m.c243 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b15 - 0.469370231236*m.b17 >= -0.469370231236)
m.c244 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b14 - 0.469370231236*m.b18 >= -0.469370231236)
m.c245 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b15 - 0.469370231236*m.b19 >= -0.469370231236)
m.c246 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b14 - 0.469370231236*m.b20 >= -0.469370231236)
m.c247 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b15 - 0.469370231236*m.b21 >= -0.469370231236)
m.c248 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b14 - 0.469370231236*m.b22 >= -0.469370231236)
m.c249 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b15 - 0.469370231236*m.b23 >= -0.469370231236)
m.c250 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b14 - 1.436936830729*m.b24 >= -1.436936830729)
m.c251 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b15 - 1.436936830729*m.b25 >= -1.436936830729)
m.c252 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b14 - 1.436936830729*m.b26 >= -1.436936830729)
m.c253 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b15 - 1.436936830729*m.b27 >= -1.436936830729)
m.c254 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b14 - 1.436936830729*m.b28 >= -1.436936830729)
m.c255 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b15 - 1.436936830729*m.b29 >= -1.436936830729)
m.c256 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b14 - 1.436936830729*m.b30 >= -1.436936830729)
m.c257 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b15 - 1.436936830729*m.b31 >= -1.436936830729)
m.c258 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b14 - 3.484990776969*m.b32 >= -3.484990776969)
m.c259 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b15 - 3.484990776969*m.b33 >= -3.484990776969)
m.c260 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b14 - 3.484990776969*m.b34 >= -3.484990776969)
m.c261 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b15 - 3.484990776969*m.b35 >= -3.484990776969)
m.c262 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b14 - 3.484990776969*m.b36 >= -3.484990776969)
m.c263 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b15 - 3.484990776969*m.b37 >= -3.484990776969)
m.c264 = Constraint(expr=m.x209**2 + m.x210**2 + m.x233**2 + m.x234**2 - 2*m.x209*m.x233 - 2*m.x210*m.x234
- 5.174182750489*m.b14 - 5.174182750489*m.b38 >= -5.174182750489)
m.c265 = Constraint(expr=m.x209**2 + m.x210**2 + m.x233**2 + m.x234**2 - 2*m.x209*m.x233 - 2*m.x210*m.x234
- 5.174182750489*m.b15 - 5.174182750489*m.b39 >= -5.174182750489)
m.c266 = Constraint(expr=m.x209**2 + m.x210**2 + m.x235**2 + m.x236**2 - 2*m.x209*m.x235 - 2*m.x210*m.x236
- 5.174182750489*m.b14 - 5.174182750489*m.b40 >= -5.174182750489)
m.c267 = Constraint(expr=m.x209**2 + m.x210**2 + m.x235**2 + m.x236**2 - 2*m.x209*m.x235 - 2*m.x210*m.x236
- 5.174182750489*m.b15 - 5.174182750489*m.b41 >= -5.174182750489)
m.c268 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b2 - 0.887203867225*m.b16 >= -0.887203867225)
m.c269 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b3 - 0.887203867225*m.b17 >= -0.887203867225)
m.c270 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b4 - 0.887203867225*m.b16 >= -0.887203867225)
m.c271 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b5 - 0.887203867225*m.b17 >= -0.887203867225)
m.c272 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b6 - 0.887203867225*m.b16 >= -0.887203867225)
m.c273 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b7 - 0.887203867225*m.b17 >= -0.887203867225)
m.c274 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b8 - 0.887203867225*m.b16 >= -0.887203867225)
m.c275 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b9 - 0.887203867225*m.b17 >= -0.887203867225)
m.c276 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b10 - 0.887203867225*m.b16 >= -0.887203867225)
m.c277 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b11 - 0.887203867225*m.b17 >= -0.887203867225)
m.c278 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b12 - 0.887203867225*m.b16 >= -0.887203867225)
m.c279 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b13 - 0.887203867225*m.b17 >= -0.887203867225)
m.c280 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b14 - 0.469370231236*m.b16 >= -0.469370231236)
m.c281 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b15 - 0.469370231236*m.b17 >= -0.469370231236)
m.c282 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b16 - 0.469370231236*m.b18 >= -0.469370231236)
m.c283 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b17 - 0.469370231236*m.b19 >= -0.469370231236)
m.c284 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b16 - 0.469370231236*m.b20 >= -0.469370231236)
m.c285 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b17 - 0.469370231236*m.b21 >= -0.469370231236)
m.c286 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b16 - 0.469370231236*m.b22 >= -0.469370231236)
m.c287 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b17 - 0.469370231236*m.b23 >= -0.469370231236)
m.c288 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b16 - 1.436936830729*m.b24 >= -1.436936830729)
m.c289 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b17 - 1.436936830729*m.b25 >= -1.436936830729)
m.c290 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b16 - 1.436936830729*m.b26 >= -1.436936830729)
m.c291 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b17 - 1.436936830729*m.b27 >= -1.436936830729)
m.c292 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b16 - 1.436936830729*m.b28 >= -1.436936830729)
m.c293 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b17 - 1.436936830729*m.b29 >= -1.436936830729)
m.c294 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b16 - 1.436936830729*m.b30 >= -1.436936830729)
m.c295 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b17 - 1.436936830729*m.b31 >= -1.436936830729)
m.c296 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b16 - 3.484990776969*m.b32 >= -3.484990776969)
m.c297 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b17 - 3.484990776969*m.b33 >= -3.484990776969)
m.c298 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b16 - 3.484990776969*m.b34 >= -3.484990776969)
m.c299 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b17 - 3.484990776969*m.b35 >= -3.484990776969)
m.c300 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b16 - 3.484990776969*m.b36 >= -3.484990776969)
m.c301 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b17 - 3.484990776969*m.b37 >= -3.484990776969)
m.c302 = Constraint(expr=m.x211**2 + m.x212**2 + m.x233**2 + m.x234**2 - 2*m.x211*m.x233 - 2*m.x212*m.x234
- 5.174182750489*m.b16 - 5.174182750489*m.b38 >= -5.174182750489)
m.c303 = Constraint(expr=m.x211**2 + m.x212**2 + m.x233**2 + m.x234**2 - 2*m.x211*m.x233 - 2*m.x212*m.x234
- 5.174182750489*m.b17 - 5.174182750489*m.b39 >= -5.174182750489)
m.c304 = Constraint(expr=m.x211**2 + m.x212**2 + m.x235**2 + m.x236**2 - 2*m.x211*m.x235 - 2*m.x212*m.x236
- 5.174182750489*m.b16 - 5.174182750489*m.b40 >= -5.174182750489)
m.c305 = Constraint(expr=m.x211**2 + m.x212**2 + m.x235**2 + m.x236**2 - 2*m.x211*m.x235 - 2*m.x212*m.x236
- 5.174182750489*m.b17 - 5.174182750489*m.b41 >= -5.174182750489)
m.c306 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b2 - 0.887203867225*m.b18 >= -0.887203867225)
m.c307 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b3 - 0.887203867225*m.b19 >= -0.887203867225)
m.c308 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b4 - 0.887203867225*m.b18 >= -0.887203867225)
m.c309 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b5 - 0.887203867225*m.b19 >= -0.887203867225)
m.c310 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b6 - 0.887203867225*m.b18 >= -0.887203867225)
m.c311 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b7 - 0.887203867225*m.b19 >= -0.887203867225)
m.c312 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b8 - 0.887203867225*m.b18 >= -0.887203867225)
m.c313 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b9 - 0.887203867225*m.b19 >= -0.887203867225)
m.c314 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b10 - 0.887203867225*m.b18 >= -0.887203867225)
m.c315 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b11 - 0.887203867225*m.b19 >= -0.887203867225)
m.c316 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b12 - 0.887203867225*m.b18 >= -0.887203867225)
m.c317 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b13 - 0.887203867225*m.b19 >= -0.887203867225)
m.c318 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b14 - 0.469370231236*m.b18 >= -0.469370231236)
m.c319 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b15 - 0.469370231236*m.b19 >= -0.469370231236)
m.c320 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b16 - 0.469370231236*m.b18 >= -0.469370231236)
m.c321 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b17 - 0.469370231236*m.b19 >= -0.469370231236)
m.c322 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b18 - 0.469370231236*m.b20 >= -0.469370231236)
m.c323 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b19 - 0.469370231236*m.b21 >= -0.469370231236)
m.c324 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b18 - 0.469370231236*m.b22 >= -0.469370231236)
m.c325 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b19 - 0.469370231236*m.b23 >= -0.469370231236)
m.c326 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b18 - 1.436936830729*m.b24 >= -1.436936830729)
m.c327 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b19 - 1.436936830729*m.b25 >= -1.436936830729)
m.c328 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b18 - 1.436936830729*m.b26 >= -1.436936830729)
m.c329 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b19 - 1.436936830729*m.b27 >= -1.436936830729)
m.c330 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b18 - 1.436936830729*m.b28 >= -1.436936830729)
m.c331 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b19 - 1.436936830729*m.b29 >= -1.436936830729)
m.c332 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b18 - 1.436936830729*m.b30 >= -1.436936830729)
m.c333 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b19 - 1.436936830729*m.b31 >= -1.436936830729)
m.c334 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b18 - 3.484990776969*m.b32 >= -3.484990776969)
m.c335 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b19 - 3.484990776969*m.b33 >= -3.484990776969)
m.c336 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b18 - 3.484990776969*m.b34 >= -3.484990776969)
m.c337 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b19 - 3.484990776969*m.b35 >= -3.484990776969)
m.c338 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b18 - 3.484990776969*m.b36 >= -3.484990776969)
m.c339 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b19 - 3.484990776969*m.b37 >= -3.484990776969)
m.c340 = Constraint(expr=m.x213**2 + m.x214**2 + m.x233**2 + m.x234**2 - 2*m.x213*m.x233 - 2*m.x214*m.x234
- 5.174182750489*m.b18 - 5.174182750489*m.b38 >= -5.174182750489)
m.c341 = Constraint(expr=m.x213**2 + m.x214**2 + m.x233**2 + m.x234**2 - 2*m.x213*m.x233 - 2*m.x214*m.x234
- 5.174182750489*m.b19 - 5.174182750489*m.b39 >= -5.174182750489)
m.c342 = Constraint(expr=m.x213**2 + m.x214**2 + m.x235**2 + m.x236**2 - 2*m.x213*m.x235 - 2*m.x214*m.x236
- 5.174182750489*m.b18 - 5.174182750489*m.b40 >= -5.174182750489)
m.c343 = Constraint(expr=m.x213**2 + m.x214**2 + m.x235**2 + m.x236**2 - 2*m.x213*m.x235 - 2*m.x214*m.x236
- 5.174182750489*m.b19 - 5.174182750489*m.b41 >= -5.174182750489)
m.c344 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b2 - 0.887203867225*m.b20 >= -0.887203867225)
m.c345 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b3 - 0.887203867225*m.b21 >= -0.887203867225)
m.c346 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b4 - 0.887203867225*m.b20 >= -0.887203867225)
m.c347 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b5 - 0.887203867225*m.b21 >= -0.887203867225)
m.c348 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b6 - 0.887203867225*m.b20 >= -0.887203867225)
m.c349 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b7 - 0.887203867225*m.b21 >= -0.887203867225)
m.c350 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x203*m.x215 - 2*m.x204*m.x216
- 0.887203867225*m.b8 - 0.887203867225*m.b20 >= -0.887203867225)
m.c351 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b9 - 0.887203867225*m.b21 >= -0.887203867225)
m.c352 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b10 - 0.887203867225*m.b20 >= -0.887203867225)
m.c353 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b11 - 0.887203867225*m.b21 >= -0.887203867225)
m.c354 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b12 - 0.887203867225*m.b20 >= -0.887203867225)
m.c355 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b13 - 0.887203867225*m.b21 >= -0.887203867225)
m.c356 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b14 - 0.469370231236*m.b20 >= -0.469370231236)
m.c357 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b15 - 0.469370231236*m.b21 >= -0.469370231236)
m.c358 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b16 - 0.469370231236*m.b20 >= -0.469370231236)
m.c359 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b17 - 0.469370231236*m.b21 >= -0.469370231236)
m.c360 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b18 - 0.469370231236*m.b20 >= -0.469370231236)
m.c361 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b19 - 0.469370231236*m.b21 >= -0.469370231236)
m.c362 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b20 - 0.469370231236*m.b22 >= -0.469370231236)
m.c363 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b21 - 0.469370231236*m.b23 >= -0.469370231236)
m.c364 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b20 - 1.436936830729*m.b24 >= -1.436936830729)
m.c365 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b21 - 1.436936830729*m.b25 >= -1.436936830729)
m.c366 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b20 - 1.436936830729*m.b26 >= -1.436936830729)
m.c367 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b21 - 1.436936830729*m.b27 >= -1.436936830729)
m.c368 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b20 - 1.436936830729*m.b28 >= -1.436936830729)
m.c369 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b21 - 1.436936830729*m.b29 >= -1.436936830729)
m.c370 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b20 - 1.436936830729*m.b30 >= -1.436936830729)
m.c371 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b21 - 1.436936830729*m.b31 >= -1.436936830729)
m.c372 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b20 - 3.484990776969*m.b32 >= -3.484990776969)
m.c373 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b21 - 3.484990776969*m.b33 >= -3.484990776969)
m.c374 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b20 - 3.484990776969*m.b34 >= -3.484990776969)
m.c375 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b21 - 3.484990776969*m.b35 >= -3.484990776969)
m.c376 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b20 - 3.484990776969*m.b36 >= -3.484990776969)
m.c377 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b21 - 3.484990776969*m.b37 >= -3.484990776969)
m.c378 = Constraint(expr=m.x215**2 + m.x216**2 + m.x233**2 + m.x234**2 - 2*m.x215*m.x233 - 2*m.x216*m.x234
- 5.174182750489*m.b20 - 5.174182750489*m.b38 >= -5.174182750489)
m.c379 = Constraint(expr=m.x215**2 + m.x216**2 + m.x233**2 + m.x234**2 - 2*m.x215*m.x233 - 2*m.x216*m.x234
- 5.174182750489*m.b21 - 5.174182750489*m.b39 >= -5.174182750489)
m.c380 = Constraint(expr=m.x215**2 + m.x216**2 + m.x235**2 + m.x236**2 - 2*m.x215*m.x235 - 2*m.x216*m.x236
- 5.174182750489*m.b20 - 5.174182750489*m.b40 >= -5.174182750489)
m.c381 = Constraint(expr=m.x215**2 + m.x216**2 + m.x235**2 + m.x236**2 - 2*m.x215*m.x235 - 2*m.x216*m.x236
- 5.174182750489*m.b21 - 5.174182750489*m.b41 >= -5.174182750489)
m.c382 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b2 - 0.887203867225*m.b22 >= -0.887203867225)
m.c383 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b3 - 0.887203867225*m.b23 >= -0.887203867225)
m.c384 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b4 - 0.887203867225*m.b22 >= -0.887203867225)
m.c385 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b5 - 0.887203867225*m.b23 >= -0.887203867225)
m.c386 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b6 - 0.887203867225*m.b22 >= -0.887203867225)
m.c387 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b7 - 0.887203867225*m.b23 >= -0.887203867225)
m.c388 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b8 - 0.887203867225*m.b22 >= -0.887203867225)
m.c389 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b9 - 0.887203867225*m.b23 >= -0.887203867225)
m.c390 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b10 - 0.887203867225*m.b22 >= -0.887203867225)
m.c391 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b11 - 0.887203867225*m.b23 >= -0.887203867225)
m.c392 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b12 - 0.887203867225*m.b22 >= -0.887203867225)
m.c393 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b13 - 0.887203867225*m.b23 >= -0.887203867225)
m.c394 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b14 - 0.469370231236*m.b22 >= -0.469370231236)
m.c395 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b15 - 0.469370231236*m.b23 >= -0.469370231236)
m.c396 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b16 - 0.469370231236*m.b22 >= -0.469370231236)
m.c397 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b17 - 0.469370231236*m.b23 >= -0.469370231236)
m.c398 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b18 - 0.469370231236*m.b22 >= -0.469370231236)
m.c399 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b19 - 0.469370231236*m.b23 >= -0.469370231236)
m.c400 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b20 - 0.469370231236*m.b22 >= -0.469370231236)
m.c401 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b21 - 0.469370231236*m.b23 >= -0.469370231236)
m.c402 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b22 - 1.436936830729*m.b24 >= -1.436936830729)
m.c403 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b23 - 1.436936830729*m.b25 >= -1.436936830729)
m.c404 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b22 - 1.436936830729*m.b26 >= -1.436936830729)
m.c405 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b23 - 1.436936830729*m.b27 >= -1.436936830729)
m.c406 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b22 - 1.436936830729*m.b28 >= -1.436936830729)
m.c407 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b23 - 1.436936830729*m.b29 >= -1.436936830729)
m.c408 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b22 - 1.436936830729*m.b30 >= -1.436936830729)
m.c409 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b23 - 1.436936830729*m.b31 >= -1.436936830729)
m.c410 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b22 - 3.484990776969*m.b32 >= -3.484990776969)
m.c411 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b23 - 3.484990776969*m.b33 >= -3.484990776969)
m.c412 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b22 - 3.484990776969*m.b34 >= -3.484990776969)
m.c413 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b23 - 3.484990776969*m.b35 >= -3.484990776969)
m.c414 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b22 - 3.484990776969*m.b36 >= -3.484990776969)
m.c415 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b23 - 3.484990776969*m.b37 >= -3.484990776969)
m.c416 = Constraint(expr=m.x217**2 + m.x218**2 + m.x233**2 + m.x234**2 - 2*m.x217*m.x233 - 2*m.x218*m.x234
- 5.174182750489*m.b22 - 5.174182750489*m.b38 >= -5.174182750489)
m.c417 = Constraint(expr=m.x217**2 + m.x218**2 + m.x233**2 + m.x234**2 - 2*m.x217*m.x233 - 2*m.x218*m.x234
- 5.174182750489*m.b23 - 5.174182750489*m.b39 >= -5.174182750489)
m.c418 = Constraint(expr=m.x217**2 + m.x218**2 + m.x235**2 + m.x236**2 - 2*m.x217*m.x235 - 2*m.x218*m.x236
- 5.174182750489*m.b22 - 5.174182750489*m.b40 >= -5.174182750489)
m.c419 = Constraint(expr=m.x217**2 + m.x218**2 + m.x235**2 + m.x236**2 - 2*m.x217*m.x235 - 2*m.x218*m.x236
- 5.174182750489*m.b23 - 5.174182750489*m.b41 >= -5.174182750489)
m.c420 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b2 - 2.118573403024*m.b24 >= -2.118573403024)
m.c421 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b3 - 2.118573403024*m.b25 >= -2.118573403024)
m.c422 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b4 - 2.118573403024*m.b24 >= -2.118573403024)
m.c423 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b5 - 2.118573403024*m.b25 >= -2.118573403024)
m.c424 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b6 - 2.118573403024*m.b24 >= -2.118573403024)
m.c425 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b7 - 2.118573403024*m.b25 >= -2.118573403024)
m.c426 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b8 - 2.118573403024*m.b24 >= -2.118573403024)
m.c427 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b9 - 2.118573403024*m.b25 >= -2.118573403024)
m.c428 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b10 - 2.118573403024*m.b24 >= -2.118573403024)
m.c429 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b11 - 2.118573403024*m.b25 >= -2.118573403024)
m.c430 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b12 - 2.118573403024*m.b24 >= -2.118573403024)
m.c431 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b13 - 2.118573403024*m.b25 >= -2.118573403024)
m.c432 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b14 - 1.436936830729*m.b24 >= -1.436936830729)
m.c433 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b15 - 1.436936830729*m.b25 >= -1.436936830729)
m.c434 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b16 - 1.436936830729*m.b24 >= -1.436936830729)
m.c435 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b17 - 1.436936830729*m.b25 >= -1.436936830729)
m.c436 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b18 - 1.436936830729*m.b24 >= -1.436936830729)
m.c437 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b19 - 1.436936830729*m.b25 >= -1.436936830729)
m.c438 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b20 - 1.436936830729*m.b24 >= -1.436936830729)
m.c439 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b21 - 1.436936830729*m.b25 >= -1.436936830729)
m.c440 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b22 - 1.436936830729*m.b24 >= -1.436936830729)
m.c441 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b23 - 1.436936830729*m.b25 >= -1.436936830729)
m.c442 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b24 - 2.9321082756*m.b26 >= -2.9321082756)
m.c443 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b25 - 2.9321082756*m.b27 >= -2.9321082756)
m.c444 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b24 - 2.9321082756*m.b28 >= -2.9321082756)
m.c445 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b25 - 2.9321082756*m.b29 >= -2.9321082756)
m.c446 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b24 - 2.9321082756*m.b30 >= -2.9321082756)
m.c447 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b25 - 2.9321082756*m.b31 >= -2.9321082756)
m.c448 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b24 - 5.6664469849*m.b32 >= -5.6664469849)
m.c449 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b25 - 5.6664469849*m.b33 >= -5.6664469849)
m.c450 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b24 - 5.6664469849*m.b34 >= -5.6664469849)
m.c451 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b25 - 5.6664469849*m.b35 >= -5.6664469849)
m.c452 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b24 - 5.6664469849*m.b36 >= -5.6664469849)
m.c453 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b25 - 5.6664469849*m.b37 >= -5.6664469849)
m.c454 = Constraint(expr=m.x219**2 + m.x220**2 + m.x233**2 + m.x234**2 - 2*m.x219*m.x233 - 2*m.x220*m.x234
- 7.77461689*m.b24 - 7.77461689*m.b38 >= -7.77461689)
m.c455 = Constraint(expr=m.x219**2 + m.x220**2 + m.x233**2 + m.x234**2 - 2*m.x219*m.x233 - 2*m.x220*m.x234
- 7.77461689*m.b25 - 7.77461689*m.b39 >= -7.77461689)
m.c456 = Constraint(expr=m.x219**2 + m.x220**2 + m.x235**2 + m.x236**2 - 2*m.x219*m.x235 - 2*m.x220*m.x236
- 7.77461689*m.b24 - 7.77461689*m.b40 >= -7.77461689)
m.c457 = Constraint(expr=m.x219**2 + m.x220**2 + m.x235**2 + m.x236**2 - 2*m.x219*m.x235 - 2*m.x220*m.x236
- 7.77461689*m.b25 - 7.77461689*m.b41 >= -7.77461689)
m.c458 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b2 - 2.118573403024*m.b26 >= -2.118573403024)
m.c459 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b3 - 2.118573403024*m.b27 >= -2.118573403024)
m.c460 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b4 - 2.118573403024*m.b26 >= -2.118573403024)
m.c461 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b5 - 2.118573403024*m.b27 >= -2.118573403024)
m.c462 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b6 - 2.118573403024*m.b26 >= -2.118573403024)
m.c463 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b7 - 2.118573403024*m.b27 >= -2.118573403024)
m.c464 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x204*m.x222 - 2*m.x203*m.x221
- 2.118573403024*m.b8 - 2.118573403024*m.b26 >= -2.118573403024)
m.c465 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b9 - 2.118573403024*m.b27 >= -2.118573403024)
m.c466 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b10 - 2.118573403024*m.b26 >= -2.118573403024)
m.c467 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b11 - 2.118573403024*m.b27 >= -2.118573403024)
m.c468 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b12 - 2.118573403024*m.b26 >= -2.118573403024)
m.c469 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b13 - 2.118573403024*m.b27 >= -2.118573403024)
m.c470 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b14 - 1.436936830729*m.b26 >= -1.436936830729)
m.c471 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b15 - 1.436936830729*m.b27 >= -1.436936830729)
m.c472 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b16 - 1.436936830729*m.b26 >= -1.436936830729)
m.c473 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b17 - 1.436936830729*m.b27 >= -1.436936830729)
m.c474 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b18 - 1.436936830729*m.b26 >= -1.436936830729)
m.c475 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b19 - 1.436936830729*m.b27 >= -1.436936830729)
m.c476 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b20 - 1.436936830729*m.b26 >= -1.436936830729)
m.c477 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b21 - 1.436936830729*m.b27 >= -1.436936830729)
m.c478 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b22 - 1.436936830729*m.b26 >= -1.436936830729)
m.c479 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b23 - 1.436936830729*m.b27 >= -1.436936830729)
m.c480 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b24 - 2.9321082756*m.b26 >= -2.9321082756)
m.c481 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b25 - 2.9321082756*m.b27 >= -2.9321082756)
m.c482 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b26 - 2.9321082756*m.b28 >= -2.9321082756)
m.c483 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b27 - 2.9321082756*m.b29 >= -2.9321082756)
m.c484 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b26 - 2.9321082756*m.b30 >= -2.9321082756)
m.c485 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b27 - 2.9321082756*m.b31 >= -2.9321082756)
m.c486 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b26 - 5.6664469849*m.b32 >= -5.6664469849)
m.c487 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b27 - 5.6664469849*m.b33 >= -5.6664469849)
m.c488 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b26 - 5.6664469849*m.b34 >= -5.6664469849)
m.c489 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b27 - 5.6664469849*m.b35 >= -5.6664469849)
m.c490 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b26 - 5.6664469849*m.b36 >= -5.6664469849)
m.c491 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b27 - 5.6664469849*m.b37 >= -5.6664469849)
m.c492 = Constraint(expr=m.x221**2 + m.x222**2 + m.x233**2 + m.x234**2 - 2*m.x221*m.x233 - 2*m.x222*m.x234
- 7.77461689*m.b26 - 7.77461689*m.b38 >= -7.77461689)
m.c493 = Constraint(expr=m.x221**2 + m.x222**2 + m.x233**2 + m.x234**2 - 2*m.x221*m.x233 - 2*m.x222*m.x234
- 7.77461689*m.b27 - 7.77461689*m.b39 >= -7.77461689)
m.c494 = Constraint(expr=m.x221**2 + m.x222**2 + m.x235**2 + m.x236**2 - 2*m.x221*m.x235 - 2*m.x222*m.x236
- 7.77461689*m.b26 - 7.77461689*m.b40 >= -7.77461689)
m.c495 = Constraint(expr=m.x221**2 + m.x222**2 + m.x235**2 + m.x236**2 - 2*m.x221*m.x235 - 2*m.x222*m.x236
- 7.77461689*m.b27 - 7.77461689*m.b41 >= -7.77461689)
m.c496 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b2 - 2.118573403024*m.b28 >= -2.118573403024)
m.c497 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b3 - 2.118573403024*m.b29 >= -2.118573403024)
m.c498 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b4 - 2.118573403024*m.b28 >= -2.118573403024)
m.c499 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b5 - 2.118573403024*m.b29 >= -2.118573403024)
m.c500 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b6 - 2.118573403024*m.b28 >= -2.118573403024)
m.c501 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x202*m.x224 - 2*m.x201*m.x223
- 2.118573403024*m.b7 - 2.118573403024*m.b29 >= -2.118573403024)
m.c502 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b8 - 2.118573403024*m.b28 >= -2.118573403024)
m.c503 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b9 - 2.118573403024*m.b29 >= -2.118573403024)
m.c504 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b10 - 2.118573403024*m.b28 >= -2.118573403024)
m.c505 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b11 - 2.118573403024*m.b29 >= -2.118573403024)
m.c506 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b12 - 2.118573403024*m.b28 >= -2.118573403024)
m.c507 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b13 - 2.118573403024*m.b29 >= -2.118573403024)
m.c508 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b14 - 1.436936830729*m.b28 >= -1.436936830729)
m.c509 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b15 - 1.436936830729*m.b29 >= -1.436936830729)
m.c510 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b16 - 1.436936830729*m.b28 >= -1.436936830729)
m.c511 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b17 - 1.436936830729*m.b29 >= -1.436936830729)
m.c512 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b18 - 1.436936830729*m.b28 >= -1.436936830729)
m.c513 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b19 - 1.436936830729*m.b29 >= -1.436936830729)
m.c514 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b20 - 1.436936830729*m.b28 >= -1.436936830729)
m.c515 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b21 - 1.436936830729*m.b29 >= -1.436936830729)
m.c516 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b22 - 1.436936830729*m.b28 >= -1.436936830729)
m.c517 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b23 - 1.436936830729*m.b29 >= -1.436936830729)
m.c518 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b24 - 2.9321082756*m.b28 >= -2.9321082756)
m.c519 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b25 - 2.9321082756*m.b29 >= -2.9321082756)
m.c520 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b26 - 2.9321082756*m.b28 >= -2.9321082756)
m.c521 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b27 - 2.9321082756*m.b29 >= -2.9321082756)
m.c522 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b28 - 2.9321082756*m.b30 >= -2.9321082756)
m.c523 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b29 - 2.9321082756*m.b31 >= -2.9321082756)
m.c524 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b28 - 5.6664469849*m.b32 >= -5.6664469849)
m.c525 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b29 - 5.6664469849*m.b33 >= -5.6664469849)
m.c526 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b28 - 5.6664469849*m.b34 >= -5.6664469849)
m.c527 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b29 - 5.6664469849*m.b35 >= -5.6664469849)
m.c528 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b28 - 5.6664469849*m.b36 >= -5.6664469849)
m.c529 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b29 - 5.6664469849*m.b37 >= -5.6664469849)
m.c530 = Constraint(expr=m.x223**2 + m.x224**2 + m.x233**2 + m.x234**2 - 2*m.x223*m.x233 - 2*m.x224*m.x234
- 7.77461689*m.b28 - 7.77461689*m.b38 >= -7.77461689)
m.c531 = Constraint(expr=m.x223**2 + m.x224**2 + m.x233**2 + m.x234**2 - 2*m.x223*m.x233 - 2*m.x224*m.x234
- 7.77461689*m.b29 - 7.77461689*m.b39 >= -7.77461689)
m.c532 = Constraint(expr=m.x223**2 + m.x224**2 + m.x235**2 + m.x236**2 - 2*m.x223*m.x235 - 2*m.x224*m.x236
- 7.77461689*m.b28 - 7.77461689*m.b40 >= -7.77461689)
m.c533 = Constraint(expr=m.x223**2 + m.x224**2 + m.x235**2 + m.x236**2 - 2*m.x223*m.x235 - 2*m.x224*m.x236
- 7.77461689*m.b29 - 7.77461689*m.b41 >= -7.77461689)
m.c534 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b2 - 2.118573403024*m.b30 >= -2.118573403024)
m.c535 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b3 - 2.118573403024*m.b31 >= -2.118573403024)
m.c536 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b4 - 2.118573403024*m.b30 >= -2.118573403024)
m.c537 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b5 - 2.118573403024*m.b31 >= -2.118573403024)
m.c538 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b6 - 2.118573403024*m.b30 >= -2.118573403024)
m.c539 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b7 - 2.118573403024*m.b31 >= -2.118573403024)
m.c540 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b8 - 2.118573403024*m.b30 >= -2.118573403024)
m.c541 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b9 - 2.118573403024*m.b31 >= -2.118573403024)
m.c542 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b10 - 2.118573403024*m.b30 >= -2.118573403024)
m.c543 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b11 - 2.118573403024*m.b31 >= -2.118573403024)
m.c544 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b12 - 2.118573403024*m.b30 >= -2.118573403024)
m.c545 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b13 - 2.118573403024*m.b31 >= -2.118573403024)
m.c546 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b14 - 1.436936830729*m.b30 >= -1.436936830729)
m.c547 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b15 - 1.436936830729*m.b31 >= -1.436936830729)
m.c548 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b16 - 1.436936830729*m.b30 >= -1.436936830729)
m.c549 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b17 - 1.436936830729*m.b31 >= -1.436936830729)
m.c550 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b18 - 1.436936830729*m.b30 >= -1.436936830729)
m.c551 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b19 - 1.436936830729*m.b31 >= -1.436936830729)
m.c552 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b20 - 1.436936830729*m.b30 >= -1.436936830729)
m.c553 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b21 - 1.436936830729*m.b31 >= -1.436936830729)
m.c554 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b22 - 1.436936830729*m.b30 >= -1.436936830729)
m.c555 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b23 - 1.436936830729*m.b31 >= -1.436936830729)
m.c556 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b24 - 2.9321082756*m.b30 >= -2.9321082756)
m.c557 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b25 - 2.9321082756*m.b31 >= -2.9321082756)
m.c558 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b26 - 2.9321082756*m.b30 >= -2.9321082756)
m.c559 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b27 - 2.9321082756*m.b31 >= -2.9321082756)
m.c560 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b28 - 2.9321082756*m.b30 >= -2.9321082756)
m.c561 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b29 - 2.9321082756*m.b31 >= -2.9321082756)
m.c562 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b30 - 5.6664469849*m.b32 >= -5.6664469849)
m.c563 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b31 - 5.6664469849*m.b33 >= -5.6664469849)
m.c564 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b30 - 5.6664469849*m.b34 >= -5.6664469849)
m.c565 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b31 - 5.6664469849*m.b35 >= -5.6664469849)
m.c566 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b30 - 5.6664469849*m.b36 >= -5.6664469849)
m.c567 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b31 - 5.6664469849*m.b37 >= -5.6664469849)
m.c568 = Constraint(expr=m.x225**2 + m.x226**2 + m.x233**2 + m.x234**2 - 2*m.x225*m.x233 - 2*m.x226*m.x234
- 7.77461689*m.b30 - 7.77461689*m.b38 >= -7.77461689)
m.c569 = Constraint(expr=m.x225**2 + m.x226**2 + m.x233**2 + m.x234**2 - 2*m.x225*m.x233 - 2*m.x226*m.x234
- 7.77461689*m.b31 - 7.77461689*m.b39 >= -7.77461689)
m.c570 = Constraint(expr=m.x225**2 + m.x226**2 + m.x235**2 + m.x236**2 - 2*m.x225*m.x235 - 2*m.x226*m.x236
- 7.77461689*m.b30 - 7.77461689*m.b40 >= -7.77461689)
m.c571 = Constraint(expr=m.x225**2 + m.x226**2 + m.x235**2 + m.x236**2 - 2*m.x225*m.x235 - 2*m.x226*m.x236
- 7.77461689*m.b31 - 7.77461689*m.b41 >= -7.77461689)
m.c572 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x197*m.x227 - 2*m.x198*m.x228
- 4.509770398884*m.b2 - 4.509770398884*m.b32 >= -4.509770398884)
m.c573 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x197*m.x227 - 2*m.x198*m.x228
- 4.509770398884*m.b3 - 4.509770398884*m.b33 >= -4.509770398884)
m.c574 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b4 - 4.509770398884*m.b32 >= -4.509770398884)
m.c575 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b5 - 4.509770398884*m.b33 >= -4.509770398884)
m.c576 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b6 - 4.509770398884*m.b32 >= -4.509770398884)
m.c577 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b7 - 4.509770398884*m.b33 >= -4.509770398884)
m.c578 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x203*m.x227 - 2*m.x204*m.x228
- 4.509770398884*m.b8 - 4.509770398884*m.b32 >= -4.509770398884)
m.c579 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x203*m.x227 - 2*m.x204*m.x228
- 4.509770398884*m.b9 - 4.509770398884*m.b33 >= -4.509770398884)
m.c580 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b10 - 4.509770398884*m.b32 >= -4.509770398884)
m.c581 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b11 - 4.509770398884*m.b33 >= -4.509770398884)
m.c582 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b12 - 4.509770398884*m.b32 >= -4.509770398884)
m.c583 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b13 - 4.509770398884*m.b33 >= -4.509770398884)
m.c584 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b14 - 3.484990776969*m.b32 >= -3.484990776969)
m.c585 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b15 - 3.484990776969*m.b33 >= -3.484990776969)
m.c586 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b16 - 3.484990776969*m.b32 >= -3.484990776969)
m.c587 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b17 - 3.484990776969*m.b33 >= -3.484990776969)
m.c588 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b18 - 3.484990776969*m.b32 >= -3.484990776969)
m.c589 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b19 - 3.484990776969*m.b33 >= -3.484990776969)
m.c590 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b20 - 3.484990776969*m.b32 >= -3.484990776969)
m.c591 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b21 - 3.484990776969*m.b33 >= -3.484990776969)
m.c592 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b22 - 3.484990776969*m.b32 >= -3.484990776969)
m.c593 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b23 - 3.484990776969*m.b33 >= -3.484990776969)
m.c594 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b24 - 5.6664469849*m.b32 >= -5.6664469849)
m.c595 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b25 - 5.6664469849*m.b33 >= -5.6664469849)
m.c596 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b26 - 5.6664469849*m.b32 >= -5.6664469849)
m.c597 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b27 - 5.6664469849*m.b33 >= -5.6664469849)
m.c598 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b28 - 5.6664469849*m.b32 >= -5.6664469849)
m.c599 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b29 - 5.6664469849*m.b33 >= -5.6664469849)
m.c600 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b30 - 5.6664469849*m.b32 >= -5.6664469849)
m.c601 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b31 - 5.6664469849*m.b33 >= -5.6664469849)
m.c602 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b32 - 9.2934741904*m.b34 >= -9.2934741904)
m.c603 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b33 - 9.2934741904*m.b35 >= -9.2934741904)
m.c604 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b32 - 9.2934741904*m.b36 >= -9.2934741904)
m.c605 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b33 - 9.2934741904*m.b37 >= -9.2934741904)
m.c606 = Constraint(expr=m.x227**2 + m.x228**2 + m.x233**2 + m.x234**2 - 2*m.x227*m.x233 - 2*m.x228*m.x234
- 11.9466318321*m.b32 - 11.9466318321*m.b38 >= -11.9466318321)
m.c607 = Constraint(expr=m.x227**2 + m.x228**2 + m.x233**2 + m.x234**2 - 2*m.x227*m.x233 - 2*m.x228*m.x234
- 11.9466318321*m.b33 - 11.9466318321*m.b39 >= -11.9466318321)
m.c608 = Constraint(expr=m.x227**2 + m.x228**2 + m.x235**2 + m.x236**2 - 2*m.x227*m.x235 - 2*m.x228*m.x236
- 11.9466318321*m.b32 - 11.9466318321*m.b40 >= -11.9466318321)
m.c609 = Constraint(expr=m.x227**2 + m.x228**2 + m.x235**2 + m.x236**2 - 2*m.x227*m.x235 - 2*m.x228*m.x236
- 11.9466318321*m.b33 - 11.9466318321*m.b41 >= -11.9466318321)
m.c610 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x198*m.x230 - 2*m.x197*m.x229
- 4.509770398884*m.b2 - 4.509770398884*m.b34 >= -4.509770398884)
m.c611 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x198*m.x230 - 2*m.x197*m.x229
- 4.509770398884*m.b3 - 4.509770398884*m.b35 >= -4.509770398884)
m.c612 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b4 - 4.509770398884*m.b34 >= -4.509770398884)
m.c613 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b5 - 4.509770398884*m.b35 >= -4.509770398884)
m.c614 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x202*m.x230 - 2*m.x201*m.x229
- 4.509770398884*m.b6 - 4.509770398884*m.b34 >= -4.509770398884)
m.c615 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x202*m.x230 - 2*m.x201*m.x229
- 4.509770398884*m.b7 - 4.509770398884*m.b35 >= -4.509770398884)
m.c616 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b8 - 4.509770398884*m.b34 >= -4.509770398884)
m.c617 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b9 - 4.509770398884*m.b35 >= -4.509770398884)
m.c618 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b10 - 4.509770398884*m.b34 >= -4.509770398884)
m.c619 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b11 - 4.509770398884*m.b35 >= -4.509770398884)
m.c620 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b12 - 4.509770398884*m.b34 >= -4.509770398884)
m.c621 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b13 - 4.509770398884*m.b35 >= -4.509770398884)
m.c622 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b14 - 3.484990776969*m.b34 >= -3.484990776969)
m.c623 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b15 - 3.484990776969*m.b35 >= -3.484990776969)
m.c624 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b16 - 3.484990776969*m.b34 >= -3.484990776969)
m.c625 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b17 - 3.484990776969*m.b35 >= -3.484990776969)
m.c626 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b18 - 3.484990776969*m.b34 >= -3.484990776969)
m.c627 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b19 - 3.484990776969*m.b35 >= -3.484990776969)
m.c628 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b20 - 3.484990776969*m.b34 >= -3.484990776969)
m.c629 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b21 - 3.484990776969*m.b35 >= -3.484990776969)
m.c630 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b22 - 3.484990776969*m.b34 >= -3.484990776969)
m.c631 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b23 - 3.484990776969*m.b35 >= -3.484990776969)
m.c632 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b24 - 5.6664469849*m.b34 >= -5.6664469849)
m.c633 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b25 - 5.6664469849*m.b35 >= -5.6664469849)
m.c634 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b26 - 5.6664469849*m.b34 >= -5.6664469849)
m.c635 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b27 - 5.6664469849*m.b35 >= -5.6664469849)
m.c636 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b28 - 5.6664469849*m.b34 >= -5.6664469849)
m.c637 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b29 - 5.6664469849*m.b35 >= -5.6664469849)
m.c638 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b30 - 5.6664469849*m.b34 >= -5.6664469849)
m.c639 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b31 - 5.6664469849*m.b35 >= -5.6664469849)
m.c640 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b32 - 9.2934741904*m.b34 >= -9.2934741904)
m.c641 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b33 - 9.2934741904*m.b35 >= -9.2934741904)
m.c642 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b34 - 9.2934741904*m.b36 >= -9.2934741904)
m.c643 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b35 - 9.2934741904*m.b37 >= -9.2934741904)
m.c644 = Constraint(expr=m.x229**2 + m.x230**2 + m.x233**2 + m.x234**2 - 2*m.x229*m.x233 - 2*m.x230*m.x234
- 11.9466318321*m.b34 - 11.9466318321*m.b38 >= -11.9466318321)
m.c645 = Constraint(expr=m.x229**2 + m.x230**2 + m.x233**2 + m.x234**2 - 2*m.x229*m.x233 - 2*m.x230*m.x234
- 11.9466318321*m.b35 - 11.9466318321*m.b39 >= -11.9466318321)
m.c646 = Constraint(expr=m.x229**2 + m.x230**2 + m.x235**2 + m.x236**2 - 2*m.x229*m.x235 - 2*m.x230*m.x236
- 11.9466318321*m.b34 - 11.9466318321*m.b40 >= -11.9466318321)
m.c647 = Constraint(expr=m.x229**2 + m.x230**2 + m.x235**2 + m.x236**2 - 2*m.x229*m.x235 - 2*m.x230*m.x236
- 11.9466318321*m.b35 - 11.9466318321*m.b41 >= -11.9466318321)
m.c648 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b2 - 4.509770398884*m.b36 >= -4.509770398884)
m.c649 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b3 - 4.509770398884*m.b37 >= -4.509770398884)
m.c650 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b4 - 4.509770398884*m.b36 >= -4.509770398884)
m.c651 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b5 - 4.509770398884*m.b37 >= -4.509770398884)
m.c652 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b6 - 4.509770398884*m.b36 >= -4.509770398884)
m.c653 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b7 - 4.509770398884*m.b37 >= -4.509770398884)
m.c654 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b8 - 4.509770398884*m.b36 >= -4.509770398884)
m.c655 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b9 - 4.509770398884*m.b37 >= -4.509770398884)
m.c656 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b10 - 4.509770398884*m.b36 >= -4.509770398884)
m.c657 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b11 - 4.509770398884*m.b37 >= -4.509770398884)
m.c658 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b12 - 4.509770398884*m.b36 >= -4.509770398884)
m.c659 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b13 - 4.509770398884*m.b37 >= -4.509770398884)
m.c660 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b14 - 3.484990776969*m.b36 >= -3.484990776969)
m.c661 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b15 - 3.484990776969*m.b37 >= -3.484990776969)
m.c662 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b16 - 3.484990776969*m.b36 >= -3.484990776969)
m.c663 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b17 - 3.484990776969*m.b37 >= -3.484990776969)
m.c664 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b18 - 3.484990776969*m.b36 >= -3.484990776969)
m.c665 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b19 - 3.484990776969*m.b37 >= -3.484990776969)
m.c666 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b20 - 3.484990776969*m.b36 >= -3.484990776969)
m.c667 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b21 - 3.484990776969*m.b37 >= -3.484990776969)
m.c668 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b22 - 3.484990776969*m.b36 >= -3.484990776969)
m.c669 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b23 - 3.484990776969*m.b37 >= -3.484990776969)
m.c670 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b24 - 5.6664469849*m.b36 >= -5.6664469849)
m.c671 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b25 - 5.6664469849*m.b37 >= -5.6664469849)
m.c672 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b26 - 5.6664469849*m.b36 >= -5.6664469849)
m.c673 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b27 - 5.6664469849*m.b37 >= -5.6664469849)
m.c674 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b28 - 5.6664469849*m.b36 >= -5.6664469849)
m.c675 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b29 - 5.6664469849*m.b37 >= -5.6664469849)
m.c676 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b30 - 5.6664469849*m.b36 >= -5.6664469849)
m.c677 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b31 - 5.6664469849*m.b37 >= -5.6664469849)
m.c678 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b32 - 9.2934741904*m.b36 >= -9.2934741904)
m.c679 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b33 - 9.2934741904*m.b37 >= -9.2934741904)
m.c680 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b34 - 9.2934741904*m.b36 >= -9.2934741904)
m.c681 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b35 - 9.2934741904*m.b37 >= -9.2934741904)
m.c682 = Constraint(expr=m.x231**2 + m.x232**2 + m.x233**2 + m.x234**2 - 2*m.x231*m.x233 - 2*m.x232*m.x234
- 11.9466318321*m.b36 - 11.9466318321*m.b38 >= -11.9466318321)
m.c683 = Constraint(expr=m.x231**2 + m.x232**2 + m.x233**2 + m.x234**2 - 2*m.x231*m.x233 - 2*m.x232*m.x234
- 11.9466318321*m.b37 - 11.9466318321*m.b39 >= -11.9466318321)
m.c684 = Constraint(expr=m.x231**2 + m.x232**2 + m.x235**2 + m.x236**2 - 2*m.x231*m.x235 - 2*m.x232*m.x236
- 11.9466318321*m.b36 - 11.9466318321*m.b40 >= -11.9466318321)
m.c685 = Constraint(expr=m.x231**2 + m.x232**2 + m.x235**2 + m.x236**2 - 2*m.x231*m.x235 - 2*m.x232*m.x236
- 11.9466318321*m.b37 - 11.9466318321*m.b41 >= -11.9466318321)
m.c686 = Constraint(expr=m.x197**2 + m.x198**2 + m.x233**2 + m.x234**2 - 2*m.x198*m.x234 - 2*m.x197*m.x233
- 6.408451746064*m.b2 - 6.408451746064*m.b38 >= -6.408451746064)
m.c687 = Constraint(expr=m.x197**2 + m.x198**2 + m.x233**2 + m.x234**2 - 2*m.x198*m.x234 - 2*m.x197*m.x233
- 6.408451746064*m.b3 - 6.408451746064*m.b39 >= -6.408451746064)
m.c688 = Constraint(expr=m.x199**2 + m.x200**2 + m.x233**2 + m.x234**2 - 2*m.x199*m.x233 - 2*m.x200*m.x234
- 6.408451746064*m.b4 - 6.408451746064*m.b38 >= -6.408451746064)
m.c689 = Constraint(expr=m.x199**2 + m.x200**2 + m.x233**2 + m.x234**2 - 2*m.x199*m.x233 - 2*m.x200*m.x234
- 6.408451746064*m.b5 - 6.408451746064*m.b39 >= -6.408451746064)
m.c690 = Constraint(expr=m.x201**2 + m.x202**2 + m.x233**2 + m.x234**2 - 2*m.x202*m.x234 - 2*m.x201*m.x233
- 6.408451746064*m.b6 - 6.408451746064*m.b38 >= -6.408451746064)
m.c691 = Constraint(expr=m.x201**2 + m.x202**2 + m.x233**2 + m.x234**2 - 2*m.x202*m.x234 - 2*m.x201*m.x233
- 6.408451746064*m.b7 - 6.408451746064*m.b39 >= -6.408451746064)
m.c692 = Constraint(expr=m.x203**2 + m.x204**2 + m.x233**2 + m.x234**2 - 2*m.x204*m.x234 - 2*m.x203*m.x233
- 6.408451746064*m.b8 - 6.408451746064*m.b38 >= -6.408451746064)
m.c693 = Constraint(expr=m.x203**2 + m.x204**2 + m.x233**2 + m.x234**2 - 2*m.x204*m.x234 - 2*m.x203*m.x233
- 6.408451746064*m.b9 - 6.408451746064*m.b39 >= -6.408451746064)
m.c694 = Constraint(expr=m.x205**2 + m.x206**2 + m.x233**2 + m.x234**2 - 2*m.x205*m.x233 - 2*m.x206*m.x234
- 6.408451746064*m.b10 - 6.408451746064*m.b38 >= -6.408451746064)
m.c695 = Constraint(expr=m.x205**2 + m.x206**2 + m.x233**2 + m.x234**2 - 2*m.x205*m.x233 - 2*m.x206*m.x234
- 6.408451746064*m.b11 - 6.408451746064*m.b39 >= -6.408451746064)
m.c696 = Constraint(expr=m.x207**2 + m.x208**2 + m.x233**2 + m.x234**2 - 2*m.x207*m.x233 - 2*m.x208*m.x234
- 6.408451746064*m.b12 - 6.408451746064*m.b38 >= -6.408451746064)
m.c697 = Constraint(expr=m.x207**2 + m.x208**2 + m.x233**2 + m.x234**2 - 2*m.x207*m.x233 - 2*m.x208*m.x234
- 6.408451746064*m.b13 - 6.408451746064*m.b39 >= -6.408451746064)
m.c698 = Constraint(expr=m.x209**2 + m.x210**2 + m.x233**2 + m.x234**2 - 2*m.x209*m.x233 - 2*m.x210*m.x234
- 5.174182750489*m.b14 - 5.174182750489*m.b38 >= -5.174182750489)
m.c699 = Constraint(expr=m.x209**2 + m.x210**2 + m.x233**2 + m.x234**2 - 2*m.x209*m.x233 - 2*m.x210*m.x234
- 5.174182750489*m.b15 - 5.174182750489*m.b39 >= -5.174182750489)
m.c700 = Constraint(expr=m.x211**2 + m.x212**2 + m.x233**2 + m.x234**2 - 2*m.x211*m.x233 - 2*m.x212*m.x234
- 5.174182750489*m.b16 - 5.174182750489*m.b38 >= -5.174182750489)
m.c701 = Constraint(expr=m.x211**2 + m.x212**2 + m.x233**2 + m.x234**2 - 2*m.x211*m.x233 - 2*m.x212*m.x234
- 5.174182750489*m.b17 - 5.174182750489*m.b39 >= -5.174182750489)
m.c702 = Constraint(expr=m.x213**2 + m.x214**2 + m.x233**2 + m.x234**2 - 2*m.x213*m.x233 - 2*m.x214*m.x234
- 5.174182750489*m.b18 - 5.174182750489*m.b38 >= -5.174182750489)
m.c703 = Constraint(expr=m.x213**2 + m.x214**2 + m.x233**2 + m.x234**2 - 2*m.x213*m.x233 - 2*m.x214*m.x234
- 5.174182750489*m.b19 - 5.174182750489*m.b39 >= -5.174182750489)
m.c704 = Constraint(expr=m.x215**2 + m.x216**2 + m.x233**2 + m.x234**2 - 2*m.x215*m.x233 - 2*m.x216*m.x234
- 5.174182750489*m.b20 - 5.174182750489*m.b38 >= -5.174182750489)
m.c705 = Constraint(expr=m.x215**2 + m.x216**2 + m.x233**2 + m.x234**2 - 2*m.x215*m.x233 - 2*m.x216*m.x234
- 5.174182750489*m.b21 - 5.174182750489*m.b39 >= -5.174182750489)
m.c706 = Constraint(expr=m.x217**2 + m.x218**2 + m.x233**2 + m.x234**2 - 2*m.x217*m.x233 - 2*m.x218*m.x234
- 5.174182750489*m.b22 - 5.174182750489*m.b38 >= -5.174182750489)
m.c707 = Constraint(expr=m.x217**2 + m.x218**2 + m.x233**2 + m.x234**2 - 2*m.x217*m.x233 - 2*m.x218*m.x234
- 5.174182750489*m.b23 - 5.174182750489*m.b39 >= -5.174182750489)
m.c708 = Constraint(expr=m.x219**2 + m.x220**2 + m.x233**2 + m.x234**2 - 2*m.x219*m.x233 - 2*m.x220*m.x234
- 7.77461689*m.b24 - 7.77461689*m.b38 >= -7.77461689)
m.c709 = Constraint(expr=m.x219**2 + m.x220**2 + m.x233**2 + m.x234**2 - 2*m.x219*m.x233 - 2*m.x220*m.x234
- 7.77461689*m.b25 - 7.77461689*m.b39 >= -7.77461689)
m.c710 = Constraint(expr=m.x221**2 + m.x222**2 + m.x233**2 + m.x234**2 - 2*m.x221*m.x233 - 2*m.x222*m.x234
- 7.77461689*m.b26 - 7.77461689*m.b38 >= -7.77461689)
m.c711 = Constraint(expr=m.x221**2 + m.x222**2 + m.x233**2 + m.x234**2 - 2*m.x221*m.x233 - 2*m.x222*m.x234
- 7.77461689*m.b27 - 7.77461689*m.b39 >= -7.77461689)
m.c712 = Constraint(expr=m.x223**2 + m.x224**2 + m.x233**2 + m.x234**2 - 2*m.x223*m.x233 - 2*m.x224*m.x234
- 7.77461689*m.b28 - 7.77461689*m.b38 >= -7.77461689)
m.c713 = Constraint(expr=m.x223**2 + m.x224**2 + m.x233**2 + m.x234**2 - 2*m.x223*m.x233 - 2*m.x224*m.x234
- 7.77461689*m.b29 - 7.77461689*m.b39 >= -7.77461689)
m.c714 = Constraint(expr=m.x225**2 + m.x226**2 + m.x233**2 + m.x234**2 - 2*m.x225*m.x233 - 2*m.x226*m.x234
- 7.77461689*m.b30 - 7.77461689*m.b38 >= -7.77461689)
m.c715 = Constraint(expr=m.x225**2 + m.x226**2 + m.x233**2 + m.x234**2 - 2*m.x225*m.x233 - 2*m.x226*m.x234
- 7.77461689*m.b31 - 7.77461689*m.b39 >= -7.77461689)
m.c716 = Constraint(expr=m.x227**2 + m.x228**2 + m.x233**2 + m.x234**2 - 2*m.x227*m.x233 - 2*m.x228*m.x234
- 11.9466318321*m.b32 - 11.9466318321*m.b38 >= -11.9466318321)
m.c717 = Constraint(expr=m.x227**2 + m.x228**2 + m.x233**2 + m.x234**2 - 2*m.x227*m.x233 - 2*m.x228*m.x234
- 11.9466318321*m.b33 - 11.9466318321*m.b39 >= -11.9466318321)
m.c718 = Constraint(expr=m.x229**2 + m.x230**2 + m.x233**2 + m.x234**2 - 2*m.x229*m.x233 - 2*m.x230*m.x234
- 11.9466318321*m.b34 - 11.9466318321*m.b38 >= -11.9466318321)
m.c719 = Constraint(expr=m.x229**2 + m.x230**2 + m.x233**2 + m.x234**2 - 2*m.x229*m.x233 - 2*m.x230*m.x234
- 11.9466318321*m.b35 - 11.9466318321*m.b39 >= -11.9466318321)
m.c720 = Constraint(expr=m.x231**2 + m.x232**2 + m.x233**2 + m.x234**2 - 2*m.x231*m.x233 - 2*m.x232*m.x234
- 11.9466318321*m.b36 - 11.9466318321*m.b38 >= -11.9466318321)
m.c721 = Constraint(expr=m.x231**2 + m.x232**2 + m.x233**2 + m.x234**2 - 2*m.x231*m.x233 - 2*m.x232*m.x234
- 11.9466318321*m.b37 - 11.9466318321*m.b39 >= -11.9466318321)
m.c722 = Constraint(expr=m.x233**2 + m.x234**2 + m.x235**2 + m.x236**2 - 2*m.x233*m.x235 - 2*m.x234*m.x236
- 14.9325053476*m.b38 - 14.9325053476*m.b40 >= -14.9325053476)
m.c723 = Constraint(expr=m.x233**2 + m.x234**2 + m.x235**2 + m.x236**2 - 2*m.x233*m.x235 - 2*m.x234*m.x236
- 14.9325053476*m.b39 - 14.9325053476*m.b41 >= -14.9325053476)
m.c724 = Constraint(expr=m.x197**2 + m.x198**2 + m.x235**2 + m.x236**2 - 2*m.x198*m.x236 - 2*m.x197*m.x235
- 6.408451746064*m.b2 - 6.408451746064*m.b40 >= -6.408451746064)
m.c725 = Constraint(expr=m.x197**2 + m.x198**2 + m.x235**2 + m.x236**2 - 2*m.x198*m.x236 - 2*m.x197*m.x235
- 6.408451746064*m.b3 - 6.408451746064*m.b41 >= -6.408451746064)
m.c726 = Constraint(expr=m.x199**2 + m.x200**2 + m.x235**2 + m.x236**2 - 2*m.x200*m.x236 - 2*m.x199*m.x235
- 6.408451746064*m.b4 - 6.408451746064*m.b40 >= -6.408451746064)
m.c727 = Constraint(expr=m.x199**2 + m.x200**2 + m.x235**2 + m.x236**2 - 2*m.x200*m.x236 - 2*m.x199*m.x235
- 6.408451746064*m.b5 - 6.408451746064*m.b41 >= -6.408451746064)
m.c728 = Constraint(expr=m.x201**2 + m.x202**2 + m.x235**2 + m.x236**2 - 2*m.x202*m.x236 - 2*m.x201*m.x235
- 6.408451746064*m.b6 - 6.408451746064*m.b40 >= -6.408451746064)
m.c729 = Constraint(expr=m.x201**2 + m.x202**2 + m.x235**2 + m.x236**2 - 2*m.x202*m.x236 - 2*m.x201*m.x235
- 6.408451746064*m.b7 - 6.408451746064*m.b41 >= -6.408451746064)
m.c730 = Constraint(expr=m.x203**2 + m.x204**2 + m.x235**2 + m.x236**2 - 2*m.x204*m.x236 - 2*m.x203*m.x235
- 6.408451746064*m.b8 - 6.408451746064*m.b40 >= -6.408451746064)
m.c731 = Constraint(expr=m.x203**2 + m.x204**2 + m.x235**2 + m.x236**2 - 2*m.x204*m.x236 - 2*m.x203*m.x235
- 6.408451746064*m.b9 - 6.408451746064*m.b41 >= -6.408451746064)
m.c732 = Constraint(expr=m.x205**2 + m.x206**2 + m.x235**2 + m.x236**2 - 2*m.x205*m.x235 - 2*m.x206*m.x236
- 6.408451746064*m.b10 - 6.408451746064*m.b40 >= -6.408451746064)
m.c733 = Constraint(expr=m.x205**2 + m.x206**2 + m.x235**2 + m.x236**2 - 2*m.x205*m.x235 - 2*m.x206*m.x236
- 6.408451746064*m.b11 - 6.408451746064*m.b41 >= -6.408451746064)
m.c734 = Constraint(expr=m.x207**2 + m.x208**2 + m.x235**2 + m.x236**2 - 2*m.x207*m.x235 - 2*m.x208*m.x236
- 6.408451746064*m.b12 - 6.408451746064*m.b40 >= -6.408451746064)
m.c735 = Constraint(expr=m.x207**2 + m.x208**2 + m.x235**2 + m.x236**2 - 2*m.x207*m.x235 - 2*m.x208*m.x236
- 6.408451746064*m.b13 - 6.408451746064*m.b41 >= -6.408451746064)
m.c736 = Constraint(expr=m.x209**2 + m.x210**2 + m.x235**2 + m.x236**2 - 2*m.x209*m.x235 - 2*m.x210*m.x236
- 5.174182750489*m.b14 - 5.174182750489*m.b40 >= -5.174182750489)
m.c737 = Constraint(expr=m.x209**2 + m.x210**2 + m.x235**2 + m.x236**2 - 2*m.x209*m.x235 - 2*m.x210*m.x236
- 5.174182750489*m.b15 - 5.174182750489*m.b41 >= -5.174182750489)
m.c738 = Constraint(expr=m.x211**2 + m.x212**2 + m.x235**2 + m.x236**2 - 2*m.x211*m.x235 - 2*m.x212*m.x236
- 5.174182750489*m.b16 - 5.174182750489*m.b40 >= -5.174182750489)
m.c739 = Constraint(expr=m.x211**2 + m.x212**2 + m.x235**2 + m.x236**2 - 2*m.x211*m.x235 - 2*m.x212*m.x236
- 5.174182750489*m.b17 - 5.174182750489*m.b41 >= -5.174182750489)
m.c740 = Constraint(expr=m.x213**2 + m.x214**2 + m.x235**2 + m.x236**2 - 2*m.x213*m.x235 - 2*m.x214*m.x236
- 5.174182750489*m.b18 - 5.174182750489*m.b40 >= -5.174182750489)
m.c741 = Constraint(expr=m.x213**2 + m.x214**2 + m.x235**2 + m.x236**2 - 2*m.x213*m.x235 - 2*m.x214*m.x236
- 5.174182750489*m.b19 - 5.174182750489*m.b41 >= -5.174182750489)
m.c742 = Constraint(expr=m.x215**2 + m.x216**2 + m.x235**2 + m.x236**2 - 2*m.x215*m.x235 - 2*m.x216*m.x236
- 5.174182750489*m.b20 - 5.174182750489*m.b40 >= -5.174182750489)
m.c743 = Constraint(expr=m.x215**2 + m.x216**2 + m.x235**2 + m.x236**2 - 2*m.x215*m.x235 - 2*m.x216*m.x236
- 5.174182750489*m.b21 - 5.174182750489*m.b41 >= -5.174182750489)
m.c744 = Constraint(expr=m.x217**2 + m.x218**2 + m.x235**2 + m.x236**2 - 2*m.x217*m.x235 - 2*m.x218*m.x236
- 5.174182750489*m.b22 - 5.174182750489*m.b40 >= -5.174182750489)
m.c745 = Constraint(expr=m.x217**2 + m.x218**2 + m.x235**2 + m.x236**2 - 2*m.x217*m.x235 - 2*m.x218*m.x236
- 5.174182750489*m.b23 - 5.174182750489*m.b41 >= -5.174182750489)
m.c746 = Constraint(expr=m.x219**2 + m.x220**2 + m.x235**2 + m.x236**2 - 2*m.x219*m.x235 - 2*m.x220*m.x236
- 7.77461689*m.b24 - 7.77461689*m.b40 >= -7.77461689)
m.c747 = Constraint(expr=m.x219**2 + m.x220**2 + m.x235**2 + m.x236**2 - 2*m.x219*m.x235 - 2*m.x220*m.x236
- 7.77461689*m.b25 - 7.77461689*m.b41 >= -7.77461689)
m.c748 = Constraint(expr=m.x221**2 + m.x222**2 + m.x235**2 + m.x236**2 - 2*m.x221*m.x235 - 2*m.x222*m.x236
- 7.77461689*m.b26 - 7.77461689*m.b40 >= -7.77461689)
m.c749 = Constraint(expr=m.x221**2 + m.x222**2 + m.x235**2 + m.x236**2 - 2*m.x221*m.x235 - 2*m.x222*m.x236
- 7.77461689*m.b27 - 7.77461689*m.b41 >= -7.77461689)
m.c750 = Constraint(expr=m.x223**2 + m.x224**2 + m.x235**2 + m.x236**2 - 2*m.x223*m.x235 - 2*m.x224*m.x236
- 7.77461689*m.b28 - 7.77461689*m.b40 >= -7.77461689)
m.c751 = Constraint(expr=m.x223**2 + m.x224**2 + m.x235**2 + m.x236**2 - 2*m.x223*m.x235 - 2*m.x224*m.x236
- 7.77461689*m.b29 - 7.77461689*m.b41 >= -7.77461689)
m.c752 = Constraint(expr=m.x225**2 + m.x226**2 + m.x235**2 + m.x236**2 - 2*m.x225*m.x235 - 2*m.x226*m.x236
- 7.77461689*m.b30 - 7.77461689*m.b40 >= -7.77461689)
m.c753 = Constraint(expr=m.x225**2 + m.x226**2 + m.x235**2 + m.x236**2 - 2*m.x225*m.x235 - 2*m.x226*m.x236
- 7.77461689*m.b31 - 7.77461689*m.b41 >= -7.77461689)
m.c754 = Constraint(expr=m.x227**2 + m.x228**2 + m.x235**2 + m.x236**2 - 2*m.x227*m.x235 - 2*m.x228*m.x236
- 11.9466318321*m.b32 - 11.9466318321*m.b40 >= -11.9466318321)
m.c755 = Constraint(expr=m.x227**2 + m.x228**2 + m.x235**2 + m.x236**2 - 2*m.x227*m.x235 - 2*m.x228*m.x236
- 11.9466318321*m.b33 - 11.9466318321*m.b41 >= -11.9466318321)
m.c756 = Constraint(expr=m.x229**2 + m.x230**2 + m.x235**2 + m.x236**2 - 2*m.x229*m.x235 - 2*m.x230*m.x236
- 11.9466318321*m.b34 - 11.9466318321*m.b40 >= -11.9466318321)
m.c757 = Constraint(expr=m.x229**2 + m.x230**2 + m.x235**2 + m.x236**2 - 2*m.x229*m.x235 - 2*m.x230*m.x236
- 11.9466318321*m.b35 - 11.9466318321*m.b41 >= -11.9466318321)
m.c758 = Constraint(expr=m.x231**2 + m.x232**2 + m.x235**2 + m.x236**2 - 2*m.x231*m.x235 - 2*m.x232*m.x236
- 11.9466318321*m.b36 - 11.9466318321*m.b40 >= -11.9466318321)
m.c759 = Constraint(expr=m.x231**2 + m.x232**2 + m.x235**2 + m.x236**2 - 2*m.x231*m.x235 - 2*m.x232*m.x236
- 11.9466318321*m.b37 - 11.9466318321*m.b41 >= -11.9466318321)
m.c760 = Constraint(expr=m.x233**2 + m.x234**2 + m.x235**2 + m.x236**2 - 2*m.x233*m.x235 - 2*m.x234*m.x236
- 14.9325053476*m.b38 - 14.9325053476*m.b40 >= -14.9325053476)
m.c761 = Constraint(expr=m.x233**2 + m.x234**2 + m.x235**2 + m.x236**2 - 2*m.x233*m.x235 - 2*m.x234*m.x236
- 14.9325053476*m.b39 - 14.9325053476*m.b41 >= -14.9325053476)
m.c762 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b96 - 0.469370231236*m.b111 >= -0.469370231236)
m.c763 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b96 - 0.469370231236*m.b126 >= -0.469370231236)
m.c764 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b96 - 0.469370231236*m.b141 >= -0.469370231236)
m.c765 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b96 - 0.469370231236*m.b156 >= -0.469370231236)
m.c766 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b96 - 0.469370231236*m.b111 >= -0.469370231236)
m.c767 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b111 - 0.469370231236*m.b126 >= -0.469370231236)
m.c768 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b111 - 0.469370231236*m.b141 >= -0.469370231236)
m.c769 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b111 - 0.469370231236*m.b156 >= -0.469370231236)
m.c770 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b96 - 0.469370231236*m.b126 >= -0.469370231236)
m.c771 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b111 - 0.469370231236*m.b126 >= -0.469370231236)
m.c772 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b126 - 0.469370231236*m.b141 >= -0.469370231236)
m.c773 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b126 - 0.469370231236*m.b156 >= -0.469370231236)
m.c774 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b96 - 0.469370231236*m.b141 >= -0.469370231236)
m.c775 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b111 - 0.469370231236*m.b141 >= -0.469370231236)
m.c776 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b126 - 0.469370231236*m.b141 >= -0.469370231236)
m.c777 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b141 - 0.469370231236*m.b156 >= -0.469370231236)
m.c778 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b96 - 0.469370231236*m.b156 >= -0.469370231236)
m.c779 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b111 - 0.469370231236*m.b156 >= -0.469370231236)
m.c780 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b126 - 0.469370231236*m.b156 >= -0.469370231236)
m.c781 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b141 - 0.469370231236*m.b156 >= -0.469370231236)
m.c782 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b97 - 0.469370231236*m.b112 >= -0.469370231236)
m.c783 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b97 - 0.469370231236*m.b127 >= -0.469370231236)
m.c784 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b97 - 0.469370231236*m.b142 >= -0.469370231236)
m.c785 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b97 - 0.469370231236*m.b157 >= -0.469370231236)
m.c786 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b97 - 0.469370231236*m.b112 >= -0.469370231236)
m.c787 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b112 - 0.469370231236*m.b127 >= -0.469370231236)
m.c788 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b112 - 0.469370231236*m.b142 >= -0.469370231236)
m.c789 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b112 - 0.469370231236*m.b157 >= -0.469370231236)
m.c790 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b97 - 0.469370231236*m.b127 >= -0.469370231236)
m.c791 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b112 - 0.469370231236*m.b127 >= -0.469370231236)
m.c792 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b127 - 0.469370231236*m.b142 >= -0.469370231236)
m.c793 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b127 - 0.469370231236*m.b157 >= -0.469370231236)
m.c794 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b97 - 0.469370231236*m.b142 >= -0.469370231236)
m.c795 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b112 - 0.469370231236*m.b142 >= -0.469370231236)
m.c796 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b127 - 0.469370231236*m.b142 >= -0.469370231236)
m.c797 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b142 - 0.469370231236*m.b157 >= -0.469370231236)
m.c798 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b97 - 0.469370231236*m.b157 >= -0.469370231236)
m.c799 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b112 - 0.469370231236*m.b157 >= -0.469370231236)
m.c800 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b127 - 0.469370231236*m.b157 >= -0.469370231236)
m.c801 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b142 - 0.469370231236*m.b157 >= -0.469370231236)
m.c802 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b98 - 0.469370231236*m.b113 >= -0.469370231236)
m.c803 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b98 - 0.469370231236*m.b128 >= -0.469370231236)
m.c804 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b98 - 0.469370231236*m.b143 >= -0.469370231236)
m.c805 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b98 - 0.469370231236*m.b158 >= -0.469370231236)
m.c806 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b98 - 0.469370231236*m.b113 >= -0.469370231236)
m.c807 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b113 - 0.469370231236*m.b128 >= -0.469370231236)
m.c808 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b113 - 0.469370231236*m.b143 >= -0.469370231236)
m.c809 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b113 - 0.469370231236*m.b158 >= -0.469370231236)
m.c810 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b98 - 0.469370231236*m.b128 >= -0.469370231236)
m.c811 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b113 - 0.469370231236*m.b128 >= -0.469370231236)
m.c812 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b128 - 0.469370231236*m.b143 >= -0.469370231236)
m.c813 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b128 - 0.469370231236*m.b158 >= -0.469370231236)
m.c814 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b98 - 0.469370231236*m.b143 >= -0.469370231236)
m.c815 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b113 - 0.469370231236*m.b143 >= -0.469370231236)
m.c816 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b128 - 0.469370231236*m.b143 >= -0.469370231236)
m.c817 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b143 - 0.469370231236*m.b158 >= -0.469370231236)
m.c818 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b98 - 0.469370231236*m.b158 >= -0.469370231236)
m.c819 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b113 - 0.469370231236*m.b158 >= -0.469370231236)
m.c820 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b128 - 0.469370231236*m.b158 >= -0.469370231236)
m.c821 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b143 - 0.469370231236*m.b158 >= -0.469370231236)
m.c822 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b99 - 0.469370231236*m.b114 >= -0.469370231236)
m.c823 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b99 - 0.469370231236*m.b129 >= -0.469370231236)
m.c824 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b99 - 0.469370231236*m.b144 >= -0.469370231236)
m.c825 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b99 - 0.469370231236*m.b159 >= -0.469370231236)
m.c826 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b99 - 0.469370231236*m.b114 >= -0.469370231236)
m.c827 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b114 - 0.469370231236*m.b129 >= -0.469370231236)
m.c828 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b114 - 0.469370231236*m.b144 >= -0.469370231236)
m.c829 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b114 - 0.469370231236*m.b159 >= -0.469370231236)
m.c830 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b99 - 0.469370231236*m.b129 >= -0.469370231236)
m.c831 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b114 - 0.469370231236*m.b129 >= -0.469370231236)
m.c832 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b129 - 0.469370231236*m.b144 >= -0.469370231236)
m.c833 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b129 - 0.469370231236*m.b159 >= -0.469370231236)
m.c834 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b99 - 0.469370231236*m.b144 >= -0.469370231236)
m.c835 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b114 - 0.469370231236*m.b144 >= -0.469370231236)
m.c836 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b129 - 0.469370231236*m.b144 >= -0.469370231236)
m.c837 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b144 - 0.469370231236*m.b159 >= -0.469370231236)
m.c838 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b99 - 0.469370231236*m.b159 >= -0.469370231236)
m.c839 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b114 - 0.469370231236*m.b159 >= -0.469370231236)
m.c840 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b129 - 0.469370231236*m.b159 >= -0.469370231236)
m.c841 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b144 - 0.469370231236*m.b159 >= -0.469370231236)
m.c842 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b100 - 0.469370231236*m.b115 >= -0.469370231236)
m.c843 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b100 - 0.469370231236*m.b130 >= -0.469370231236)
m.c844 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b100 - 0.469370231236*m.b145 >= -0.469370231236)
m.c845 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b100 - 0.469370231236*m.b160 >= -0.469370231236)
m.c846 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b100 - 0.469370231236*m.b115 >= -0.469370231236)
m.c847 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b115 - 0.469370231236*m.b130 >= -0.469370231236)
m.c848 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b115 - 0.469370231236*m.b145 >= -0.469370231236)
m.c849 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b115 - 0.469370231236*m.b160 >= -0.469370231236)
m.c850 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b100 - 0.469370231236*m.b130 >= -0.469370231236)
m.c851 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b115 - 0.469370231236*m.b130 >= -0.469370231236)
m.c852 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b130 - 0.469370231236*m.b145 >= -0.469370231236)
m.c853 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b130 - 0.469370231236*m.b160 >= -0.469370231236)
m.c854 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b100 - 0.469370231236*m.b145 >= -0.469370231236)
m.c855 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b115 - 0.469370231236*m.b145 >= -0.469370231236)
m.c856 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b130 - 0.469370231236*m.b145 >= -0.469370231236)
m.c857 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b145 - 0.469370231236*m.b160 >= -0.469370231236)
m.c858 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b100 - 0.469370231236*m.b160 >= -0.469370231236)
m.c859 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b115 - 0.469370231236*m.b160 >= -0.469370231236)
m.c860 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b130 - 0.469370231236*m.b160 >= -0.469370231236)
m.c861 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b145 - 0.469370231236*m.b160 >= -0.469370231236)
m.c862 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b101 - 0.469370231236*m.b116 >= -0.469370231236)
m.c863 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b101 - 0.469370231236*m.b131 >= -0.469370231236)
m.c864 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b101 - 0.469370231236*m.b146 >= -0.469370231236)
m.c865 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b101 - 0.469370231236*m.b161 >= -0.469370231236)
m.c866 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b101 - 0.469370231236*m.b116 >= -0.469370231236)
m.c867 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b116 - 0.469370231236*m.b131 >= -0.469370231236)
m.c868 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b116 - 0.469370231236*m.b146 >= -0.469370231236)
m.c869 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b116 - 0.469370231236*m.b161 >= -0.469370231236)
m.c870 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b101 - 0.469370231236*m.b131 >= -0.469370231236)
m.c871 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b116 - 0.469370231236*m.b131 >= -0.469370231236)
m.c872 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b131 - 0.469370231236*m.b146 >= -0.469370231236)
m.c873 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b131 - 0.469370231236*m.b161 >= -0.469370231236)
m.c874 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b101 - 0.469370231236*m.b146 >= -0.469370231236)
m.c875 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b116 - 0.469370231236*m.b146 >= -0.469370231236)
m.c876 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b131 - 0.469370231236*m.b146 >= -0.469370231236)
m.c877 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b146 - 0.469370231236*m.b161 >= -0.469370231236)
m.c878 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b101 - 0.469370231236*m.b161 >= -0.469370231236)
m.c879 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b116 - 0.469370231236*m.b161 >= -0.469370231236)
m.c880 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b131 - 0.469370231236*m.b161 >= -0.469370231236)
m.c881 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b146 - 0.469370231236*m.b161 >= -0.469370231236)
m.c882 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b42 - 1.436939228176*m.b51 >= -1.436939228176)
m.c883 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b42 - 1.436939228176*m.b60 >= -1.436939228176)
m.c884 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b42 - 1.436939228176*m.b69 >= -1.436939228176)
m.c885 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b42 - 1.436939228176*m.b78 >= -1.436939228176)
m.c886 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b42 - 1.436939228176*m.b87 >= -1.436939228176)
m.c887 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b42 - 0.887203867225*m.b102 >= -0.887203867225)
m.c888 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b42 - 0.887203867225*m.b117 >= -0.887203867225)
m.c889 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b42 - 0.887203867225*m.b132 >= -0.887203867225)
m.c890 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b42 - 0.887203867225*m.b147 >= -0.887203867225)
m.c891 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b42 - 0.887203867225*m.b162 >= -0.887203867225)
m.c892 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b42 - 1.436939228176*m.b51 >= -1.436939228176)
m.c893 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b51 - 1.436939228176*m.b60 >= -1.436939228176)
m.c894 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b51 - 1.436939228176*m.b69 >= -1.436939228176)
m.c895 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b51 - 1.436939228176*m.b78 >= -1.436939228176)
m.c896 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b51 - 1.436939228176*m.b87 >= -1.436939228176)
m.c897 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b51 - 0.887203867225*m.b102 >= -0.887203867225)
m.c898 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b51 - 0.887203867225*m.b117 >= -0.887203867225)
m.c899 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b51 - 0.887203867225*m.b132 >= -0.887203867225)
m.c900 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b51 - 0.887203867225*m.b147 >= -0.887203867225)
m.c901 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b51 - 0.887203867225*m.b162 >= -0.887203867225)
m.c902 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b42 - 1.436939228176*m.b60 >= -1.436939228176)
m.c903 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b51 - 1.436939228176*m.b60 >= -1.436939228176)
m.c904 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b60 - 1.436939228176*m.b69 >= -1.436939228176)
m.c905 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b60 - 1.436939228176*m.b78 >= -1.436939228176)
m.c906 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b60 - 1.436939228176*m.b87 >= -1.436939228176)
m.c907 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b60 - 0.887203867225*m.b102 >= -0.887203867225)
m.c908 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b60 - 0.887203867225*m.b117 >= -0.887203867225)
m.c909 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b60 - 0.887203867225*m.b132 >= -0.887203867225)
m.c910 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b60 - 0.887203867225*m.b147 >= -0.887203867225)
m.c911 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b60 - 0.887203867225*m.b162 >= -0.887203867225)
m.c912 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b42 - 1.436939228176*m.b69 >= -1.436939228176)
m.c913 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b51 - 1.436939228176*m.b69 >= -1.436939228176)
m.c914 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b60 - 1.436939228176*m.b69 >= -1.436939228176)
m.c915 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b69 - 1.436939228176*m.b78 >= -1.436939228176)
m.c916 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b69 - 1.436939228176*m.b87 >= -1.436939228176)
m.c917 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b69 - 0.887203867225*m.b102 >= -0.887203867225)
m.c918 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b69 - 0.887203867225*m.b117 >= -0.887203867225)
m.c919 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b69 - 0.887203867225*m.b132 >= -0.887203867225)
m.c920 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b69 - 0.887203867225*m.b147 >= -0.887203867225)
m.c921 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b69 - 0.887203867225*m.b162 >= -0.887203867225)
m.c922 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b42 - 1.436939228176*m.b78 >= -1.436939228176)
m.c923 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b51 - 1.436939228176*m.b78 >= -1.436939228176)
m.c924 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b60 - 1.436939228176*m.b78 >= -1.436939228176)
m.c925 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b69 - 1.436939228176*m.b78 >= -1.436939228176)
m.c926 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b78 - 1.436939228176*m.b87 >= -1.436939228176)
m.c927 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b78 - 0.887203867225*m.b102 >= -0.887203867225)
m.c928 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b78 - 0.887203867225*m.b117 >= -0.887203867225)
m.c929 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b78 - 0.887203867225*m.b132 >= -0.887203867225)
m.c930 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b78 - 0.887203867225*m.b147 >= -0.887203867225)
m.c931 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b78 - 0.887203867225*m.b162 >= -0.887203867225)
m.c932 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b42 - 1.436939228176*m.b87 >= -1.436939228176)
m.c933 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b51 - 1.436939228176*m.b87 >= -1.436939228176)
m.c934 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b60 - 1.436939228176*m.b87 >= -1.436939228176)
m.c935 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b69 - 1.436939228176*m.b87 >= -1.436939228176)
m.c936 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b78 - 1.436939228176*m.b87 >= -1.436939228176)
m.c937 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b87 - 0.887203867225*m.b102 >= -0.887203867225)
m.c938 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b87 - 0.887203867225*m.b117 >= -0.887203867225)
m.c939 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b87 - 0.887203867225*m.b132 >= -0.887203867225)
m.c940 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b87 - 0.887203867225*m.b147 >= -0.887203867225)
m.c941 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b87 - 0.887203867225*m.b162 >= -0.887203867225)
m.c942 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b42 - 0.887203867225*m.b102 >= -0.887203867225)
m.c943 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b51 - 0.887203867225*m.b102 >= -0.887203867225)
m.c944 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b60 - 0.887203867225*m.b102 >= -0.887203867225)
m.c945 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b69 - 0.887203867225*m.b102 >= -0.887203867225)
m.c946 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b78 - 0.887203867225*m.b102 >= -0.887203867225)
m.c947 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b87 - 0.887203867225*m.b102 >= -0.887203867225)
m.c948 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b102 - 0.469370231236*m.b117 >= -0.469370231236)
m.c949 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b102 - 0.469370231236*m.b132 >= -0.469370231236)
m.c950 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b102 - 0.469370231236*m.b147 >= -0.469370231236)
m.c951 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b102 - 0.469370231236*m.b162 >= -0.469370231236)
m.c952 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b42 - 0.887203867225*m.b117 >= -0.887203867225)
m.c953 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b51 - 0.887203867225*m.b117 >= -0.887203867225)
m.c954 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b60 - 0.887203867225*m.b117 >= -0.887203867225)
m.c955 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b69 - 0.887203867225*m.b117 >= -0.887203867225)
m.c956 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b78 - 0.887203867225*m.b117 >= -0.887203867225)
m.c957 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b87 - 0.887203867225*m.b117 >= -0.887203867225)
m.c958 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b102 - 0.469370231236*m.b117 >= -0.469370231236)
m.c959 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b117 - 0.469370231236*m.b132 >= -0.469370231236)
m.c960 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b117 - 0.469370231236*m.b147 >= -0.469370231236)
m.c961 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b117 - 0.469370231236*m.b162 >= -0.469370231236)
m.c962 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b42 - 0.887203867225*m.b132 >= -0.887203867225)
m.c963 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b51 - 0.887203867225*m.b132 >= -0.887203867225)
m.c964 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b60 - 0.887203867225*m.b132 >= -0.887203867225)
m.c965 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b69 - 0.887203867225*m.b132 >= -0.887203867225)
m.c966 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b78 - 0.887203867225*m.b132 >= -0.887203867225)
m.c967 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b87 - 0.887203867225*m.b132 >= -0.887203867225)
m.c968 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b102 - 0.469370231236*m.b132 >= -0.469370231236)
m.c969 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b117 - 0.469370231236*m.b132 >= -0.469370231236)
m.c970 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b132 - 0.469370231236*m.b147 >= -0.469370231236)
m.c971 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b132 - 0.469370231236*m.b162 >= -0.469370231236)
m.c972 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b42 - 0.887203867225*m.b147 >= -0.887203867225)
m.c973 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b51 - 0.887203867225*m.b147 >= -0.887203867225)
m.c974 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b60 - 0.887203867225*m.b147 >= -0.887203867225)
m.c975 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b69 - 0.887203867225*m.b147 >= -0.887203867225)
m.c976 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b78 - 0.887203867225*m.b147 >= -0.887203867225)
m.c977 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b87 - 0.887203867225*m.b147 >= -0.887203867225)
m.c978 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b102 - 0.469370231236*m.b147 >= -0.469370231236)
m.c979 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b117 - 0.469370231236*m.b147 >= -0.469370231236)
m.c980 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b132 - 0.469370231236*m.b147 >= -0.469370231236)
m.c981 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b147 - 0.469370231236*m.b162 >= -0.469370231236)
m.c982 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b42 - 0.887203867225*m.b162 >= -0.887203867225)
m.c983 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b51 - 0.887203867225*m.b162 >= -0.887203867225)
m.c984 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b60 - 0.887203867225*m.b162 >= -0.887203867225)
m.c985 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b69 - 0.887203867225*m.b162 >= -0.887203867225)
m.c986 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b78 - 0.887203867225*m.b162 >= -0.887203867225)
m.c987 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b87 - 0.887203867225*m.b162 >= -0.887203867225)
m.c988 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b102 - 0.469370231236*m.b162 >= -0.469370231236)
m.c989 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b117 - 0.469370231236*m.b162 >= -0.469370231236)
m.c990 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b132 - 0.469370231236*m.b162 >= -0.469370231236)
m.c991 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b147 - 0.469370231236*m.b162 >= -0.469370231236)
m.c992 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b43 - 1.436939228176*m.b52 >= -1.436939228176)
m.c993 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b43 - 1.436939228176*m.b61 >= -1.436939228176)
m.c994 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b43 - 1.436939228176*m.b70 >= -1.436939228176)
m.c995 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b43 - 1.436939228176*m.b79 >= -1.436939228176)
m.c996 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b43 - 1.436939228176*m.b88 >= -1.436939228176)
m.c997 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b43 - 0.887203867225*m.b103 >= -0.887203867225)
m.c998 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b43 - 0.887203867225*m.b118 >= -0.887203867225)
m.c999 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b43 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1000 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b43 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1001 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b43 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1002 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b43 - 1.436939228176*m.b52 >= -1.436939228176)
m.c1003 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b52 - 1.436939228176*m.b61 >= -1.436939228176)
m.c1004 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b52 - 1.436939228176*m.b70 >= -1.436939228176)
m.c1005 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b52 - 1.436939228176*m.b79 >= -1.436939228176)
m.c1006 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b52 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1007 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b52 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1008 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b52 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1009 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b52 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1010 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b52 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1011 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b52 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1012 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b43 - 1.436939228176*m.b61 >= -1.436939228176)
m.c1013 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b52 - 1.436939228176*m.b61 >= -1.436939228176)
m.c1014 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b61 - 1.436939228176*m.b70 >= -1.436939228176)
m.c1015 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b61 - 1.436939228176*m.b79 >= -1.436939228176)
m.c1016 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b61 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1017 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b61 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1018 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b61 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1019 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b61 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1020 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b61 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1021 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b61 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1022 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b43 - 1.436939228176*m.b70 >= -1.436939228176)
m.c1023 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b52 - 1.436939228176*m.b70 >= -1.436939228176)
m.c1024 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b61 - 1.436939228176*m.b70 >= -1.436939228176)
m.c1025 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b70 - 1.436939228176*m.b79 >= -1.436939228176)
m.c1026 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b70 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1027 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b70 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1028 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b70 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1029 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b70 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1030 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b70 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1031 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b70 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1032 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b43 - 1.436939228176*m.b79 >= -1.436939228176)
m.c1033 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b52 - 1.436939228176*m.b79 >= -1.436939228176)
m.c1034 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b61 - 1.436939228176*m.b79 >= -1.436939228176)
m.c1035 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b70 - 1.436939228176*m.b79 >= -1.436939228176)
m.c1036 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b79 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1037 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b79 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1038 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b79 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1039 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b79 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1040 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b79 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1041 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b79 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1042 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b43 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1043 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b52 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1044 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b61 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1045 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b70 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1046 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b79 - 1.436939228176*m.b88 >= -1.436939228176)
m.c1047 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b88 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1048 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b88 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1049 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b88 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1050 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b88 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1051 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b88 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1052 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b43 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1053 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b52 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1054 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b61 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1055 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b70 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1056 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b79 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1057 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b88 - 0.887203867225*m.b103 >= -0.887203867225)
m.c1058 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b103 - 0.469370231236*m.b118 >= -0.469370231236)
m.c1059 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b103 - 0.469370231236*m.b133 >= -0.469370231236)
m.c1060 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b103 - 0.469370231236*m.b148 >= -0.469370231236)
m.c1061 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b103 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1062 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b43 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1063 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b52 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1064 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b61 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1065 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b70 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1066 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b79 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1067 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b88 - 0.887203867225*m.b118 >= -0.887203867225)
m.c1068 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b103 - 0.469370231236*m.b118 >= -0.469370231236)
m.c1069 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b118 - 0.469370231236*m.b133 >= -0.469370231236)
m.c1070 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b118 - 0.469370231236*m.b148 >= -0.469370231236)
m.c1071 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b118 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1072 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b43 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1073 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b52 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1074 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b61 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1075 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b70 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1076 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b79 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1077 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b88 - 0.887203867225*m.b133 >= -0.887203867225)
m.c1078 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b103 - 0.469370231236*m.b133 >= -0.469370231236)
m.c1079 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b118 - 0.469370231236*m.b133 >= -0.469370231236)
m.c1080 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b133 - 0.469370231236*m.b148 >= -0.469370231236)
m.c1081 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b133 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1082 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b43 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1083 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b52 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1084 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b61 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1085 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b70 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1086 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b79 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1087 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b88 - 0.887203867225*m.b148 >= -0.887203867225)
m.c1088 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b103 - 0.469370231236*m.b148 >= -0.469370231236)
m.c1089 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b118 - 0.469370231236*m.b148 >= -0.469370231236)
m.c1090 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b133 - 0.469370231236*m.b148 >= -0.469370231236)
m.c1091 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b148 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1092 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b43 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1093 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b52 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1094 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b61 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1095 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b70 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1096 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b79 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1097 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b88 - 0.887203867225*m.b163 >= -0.887203867225)
m.c1098 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b103 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1099 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b118 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1100 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b133 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1101 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b148 - 0.469370231236*m.b163 >= -0.469370231236)
m.c1102 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b44 - 1.436939228176*m.b53 >= -1.436939228176)
m.c1103 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b44 - 1.436939228176*m.b62 >= -1.436939228176)
m.c1104 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b44 - 1.436939228176*m.b71 >= -1.436939228176)
m.c1105 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b44 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1106 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b44 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1107 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b44 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1108 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b44 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1109 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b44 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1110 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b44 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1111 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b44 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1112 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b44 - 1.436939228176*m.b53 >= -1.436939228176)
m.c1113 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b53 - 1.436939228176*m.b62 >= -1.436939228176)
m.c1114 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b53 - 1.436939228176*m.b71 >= -1.436939228176)
m.c1115 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b53 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1116 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b53 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1117 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b53 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1118 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b53 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1119 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b53 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1120 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b53 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1121 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b53 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1122 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b44 - 1.436939228176*m.b62 >= -1.436939228176)
m.c1123 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b53 - 1.436939228176*m.b62 >= -1.436939228176)
m.c1124 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b62 - 1.436939228176*m.b71 >= -1.436939228176)
m.c1125 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b62 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1126 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b62 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1127 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b62 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1128 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b62 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1129 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b62 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1130 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b62 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1131 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b62 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1132 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b44 - 1.436939228176*m.b71 >= -1.436939228176)
m.c1133 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b53 - 1.436939228176*m.b71 >= -1.436939228176)
m.c1134 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b62 - 1.436939228176*m.b71 >= -1.436939228176)
m.c1135 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b71 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1136 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b71 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1137 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b71 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1138 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b71 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1139 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b71 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1140 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b71 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1141 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b71 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1142 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b44 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1143 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b53 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1144 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b62 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1145 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b71 - 1.436939228176*m.b80 >= -1.436939228176)
m.c1146 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b80 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1147 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b80 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1148 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b80 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1149 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b80 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1150 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b80 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1151 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b80 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1152 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b44 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1153 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b53 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1154 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b62 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1155 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b71 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1156 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b80 - 1.436939228176*m.b89 >= -1.436939228176)
m.c1157 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b89 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1158 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b89 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1159 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b89 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1160 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b89 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1161 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b89 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1162 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b44 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1163 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b53 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1164 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b62 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1165 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b71 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1166 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b80 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1167 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b89 - 0.887203867225*m.b104 >= -0.887203867225)
m.c1168 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b104 - 0.469370231236*m.b119 >= -0.469370231236)
m.c1169 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b104 - 0.469370231236*m.b134 >= -0.469370231236)
m.c1170 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b104 - 0.469370231236*m.b149 >= -0.469370231236)
m.c1171 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b104 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1172 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b44 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1173 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b53 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1174 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b62 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1175 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b71 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1176 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b80 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1177 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b89 - 0.887203867225*m.b119 >= -0.887203867225)
m.c1178 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b104 - 0.469370231236*m.b119 >= -0.469370231236)
m.c1179 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b119 - 0.469370231236*m.b134 >= -0.469370231236)
m.c1180 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b119 - 0.469370231236*m.b149 >= -0.469370231236)
m.c1181 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b119 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1182 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b44 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1183 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b53 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1184 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b62 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1185 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b71 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1186 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b80 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1187 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b89 - 0.887203867225*m.b134 >= -0.887203867225)
m.c1188 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b104 - 0.469370231236*m.b134 >= -0.469370231236)
m.c1189 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b119 - 0.469370231236*m.b134 >= -0.469370231236)
m.c1190 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b134 - 0.469370231236*m.b149 >= -0.469370231236)
m.c1191 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b134 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1192 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b44 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1193 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b53 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1194 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b62 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1195 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x203*m.x215 - 2*m.x204*m.x216
- 0.887203867225*m.b71 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1196 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b80 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1197 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b89 - 0.887203867225*m.b149 >= -0.887203867225)
m.c1198 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b104 - 0.469370231236*m.b149 >= -0.469370231236)
m.c1199 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b119 - 0.469370231236*m.b149 >= -0.469370231236)
m.c1200 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b134 - 0.469370231236*m.b149 >= -0.469370231236)
m.c1201 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b149 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1202 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b44 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1203 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b53 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1204 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b62 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1205 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b71 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1206 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b80 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1207 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b89 - 0.887203867225*m.b164 >= -0.887203867225)
m.c1208 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b104 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1209 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b119 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1210 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b134 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1211 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b149 - 0.469370231236*m.b164 >= -0.469370231236)
m.c1212 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b45 - 1.436939228176*m.b54 >= -1.436939228176)
m.c1213 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b45 - 1.436939228176*m.b63 >= -1.436939228176)
m.c1214 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b45 - 1.436939228176*m.b72 >= -1.436939228176)
m.c1215 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b45 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1216 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b45 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1217 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b45 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1218 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b45 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1219 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b45 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1220 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b45 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1221 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b45 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1222 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b45 - 1.436939228176*m.b54 >= -1.436939228176)
m.c1223 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b54 - 1.436939228176*m.b63 >= -1.436939228176)
m.c1224 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b54 - 1.436939228176*m.b72 >= -1.436939228176)
m.c1225 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b54 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1226 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b54 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1227 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b54 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1228 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b54 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1229 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b54 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1230 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x199*m.x215 - 2*m.x200*m.x216
- 0.887203867225*m.b54 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1231 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x200*m.x218 - 2*m.x199*m.x217
- 0.887203867225*m.b54 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1232 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b45 - 1.436939228176*m.b63 >= -1.436939228176)
m.c1233 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b54 - 1.436939228176*m.b63 >= -1.436939228176)
m.c1234 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b63 - 1.436939228176*m.b72 >= -1.436939228176)
m.c1235 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b63 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1236 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b63 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1237 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b63 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1238 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b63 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1239 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b63 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1240 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b63 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1241 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b63 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1242 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b45 - 1.436939228176*m.b72 >= -1.436939228176)
m.c1243 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b54 - 1.436939228176*m.b72 >= -1.436939228176)
m.c1244 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b63 - 1.436939228176*m.b72 >= -1.436939228176)
m.c1245 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b72 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1246 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b72 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1247 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b72 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1248 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b72 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1249 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b72 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1250 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b72 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1251 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b72 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1252 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b45 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1253 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b54 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1254 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b63 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1255 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b72 - 1.436939228176*m.b81 >= -1.436939228176)
m.c1256 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b81 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1257 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b81 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1258 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b81 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1259 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b81 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1260 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b81 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1261 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b81 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1262 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b45 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1263 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b54 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1264 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b63 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1265 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b72 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1266 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b81 - 1.436939228176*m.b90 >= -1.436939228176)
m.c1267 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b90 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1268 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b90 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1269 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b90 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1270 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b90 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1271 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b90 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1272 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b45 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1273 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b54 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1274 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b63 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1275 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b72 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1276 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b81 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1277 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b90 - 0.887203867225*m.b105 >= -0.887203867225)
m.c1278 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b105 - 0.469370231236*m.b120 >= -0.469370231236)
m.c1279 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b105 - 0.469370231236*m.b135 >= -0.469370231236)
m.c1280 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b105 - 0.469370231236*m.b150 >= -0.469370231236)
m.c1281 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b105 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1282 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b45 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1283 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b54 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1284 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b63 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1285 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b72 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1286 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b81 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1287 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b90 - 0.887203867225*m.b120 >= -0.887203867225)
m.c1288 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b105 - 0.469370231236*m.b120 >= -0.469370231236)
m.c1289 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b120 - 0.469370231236*m.b135 >= -0.469370231236)
m.c1290 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b120 - 0.469370231236*m.b150 >= -0.469370231236)
m.c1291 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b120 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1292 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b45 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1293 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b54 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1294 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b63 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1295 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b72 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1296 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b81 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1297 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b90 - 0.887203867225*m.b135 >= -0.887203867225)
m.c1298 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b105 - 0.469370231236*m.b135 >= -0.469370231236)
m.c1299 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b120 - 0.469370231236*m.b135 >= -0.469370231236)
m.c1300 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b135 - 0.469370231236*m.b150 >= -0.469370231236)
m.c1301 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b135 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1302 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b45 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1303 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x199*m.x215 - 2*m.x200*m.x216
- 0.887203867225*m.b54 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1304 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b63 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1305 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b72 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1306 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b81 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1307 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b90 - 0.887203867225*m.b150 >= -0.887203867225)
m.c1308 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b105 - 0.469370231236*m.b150 >= -0.469370231236)
m.c1309 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b120 - 0.469370231236*m.b150 >= -0.469370231236)
m.c1310 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b135 - 0.469370231236*m.b150 >= -0.469370231236)
m.c1311 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b150 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1312 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b45 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1313 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b54 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1314 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b63 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1315 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b72 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1316 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b81 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1317 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b90 - 0.887203867225*m.b165 >= -0.887203867225)
m.c1318 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b105 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1319 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b120 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1320 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b135 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1321 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b150 - 0.469370231236*m.b165 >= -0.469370231236)
m.c1322 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b46 - 1.436939228176*m.b55 >= -1.436939228176)
m.c1323 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b46 - 1.436939228176*m.b64 >= -1.436939228176)
m.c1324 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b46 - 1.436939228176*m.b73 >= -1.436939228176)
m.c1325 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b46 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1326 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b46 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1327 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b46 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1328 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b46 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1329 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b46 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1330 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b46 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1331 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b46 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1332 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b46 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1333 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b46 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1334 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b46 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1335 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b46 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1336 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b46 - 1.436939228176*m.b55 >= -1.436939228176)
m.c1337 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b55 - 1.436939228176*m.b64 >= -1.436939228176)
m.c1338 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b55 - 1.436939228176*m.b73 >= -1.436939228176)
m.c1339 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b55 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1340 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b55 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1341 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b55 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1342 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b55 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1343 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b55 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1344 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x199*m.x215 - 2*m.x200*m.x216
- 0.887203867225*m.b55 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1345 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b55 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1346 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x199*m.x219 - 2*m.x200*m.x220
- 2.118573403024*m.b55 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1347 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x199*m.x221 - 2*m.x200*m.x222
- 2.118573403024*m.b55 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1348 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b55 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1349 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b55 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1350 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b46 - 1.436939228176*m.b64 >= -1.436939228176)
m.c1351 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b55 - 1.436939228176*m.b64 >= -1.436939228176)
m.c1352 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b64 - 1.436939228176*m.b73 >= -1.436939228176)
m.c1353 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b64 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1354 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b64 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1355 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b64 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1356 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b64 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1357 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b64 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1358 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b64 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1359 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b64 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1360 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b64 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1361 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b64 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1362 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x202*m.x224 - 2*m.x201*m.x223
- 2.118573403024*m.b64 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1363 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b64 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1364 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b46 - 1.436939228176*m.b73 >= -1.436939228176)
m.c1365 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b55 - 1.436939228176*m.b73 >= -1.436939228176)
m.c1366 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b64 - 1.436939228176*m.b73 >= -1.436939228176)
m.c1367 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b73 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1368 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b73 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1369 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b73 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1370 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b73 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1371 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b73 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1372 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b73 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1373 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b73 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1374 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b73 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1375 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b73 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1376 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b73 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1377 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b73 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1378 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b46 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1379 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b55 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1380 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b64 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1381 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b73 - 1.436939228176*m.b82 >= -1.436939228176)
m.c1382 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b82 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1383 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b82 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1384 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b82 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1385 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b82 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1386 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b82 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1387 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b82 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1388 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b82 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1389 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b82 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1390 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b82 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1391 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b82 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1392 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b46 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1393 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b55 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1394 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b64 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1395 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b73 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1396 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b82 - 1.436939228176*m.b91 >= -1.436939228176)
m.c1397 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b91 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1398 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b91 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1399 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b91 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1400 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b91 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1401 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b91 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1402 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b91 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1403 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b91 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1404 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b91 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1405 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b91 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1406 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b46 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1407 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b55 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1408 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b64 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1409 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b73 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1410 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b82 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1411 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b91 - 0.887203867225*m.b106 >= -0.887203867225)
m.c1412 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b106 - 0.469370231236*m.b121 >= -0.469370231236)
m.c1413 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b106 - 0.469370231236*m.b136 >= -0.469370231236)
m.c1414 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b106 - 0.469370231236*m.b151 >= -0.469370231236)
m.c1415 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b106 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1416 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b106 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1417 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b106 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1418 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b106 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1419 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b106 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1420 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b46 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1421 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b55 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1422 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b64 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1423 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b73 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1424 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b82 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1425 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b91 - 0.887203867225*m.b121 >= -0.887203867225)
m.c1426 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b106 - 0.469370231236*m.b121 >= -0.469370231236)
m.c1427 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b121 - 0.469370231236*m.b136 >= -0.469370231236)
m.c1428 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b121 - 0.469370231236*m.b151 >= -0.469370231236)
m.c1429 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b121 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1430 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b121 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1431 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b121 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1432 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b121 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1433 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b121 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1434 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b46 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1435 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b55 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1436 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b64 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1437 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b73 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1438 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b82 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1439 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b91 - 0.887203867225*m.b136 >= -0.887203867225)
m.c1440 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b106 - 0.469370231236*m.b136 >= -0.469370231236)
m.c1441 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b121 - 0.469370231236*m.b136 >= -0.469370231236)
m.c1442 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b136 - 0.469370231236*m.b151 >= -0.469370231236)
m.c1443 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b136 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1444 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b136 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1445 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b136 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1446 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b136 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1447 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b136 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1448 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b46 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1449 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x199*m.x215 - 2*m.x200*m.x216
- 0.887203867225*m.b55 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1450 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b64 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1451 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b73 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1452 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b82 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1453 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b91 - 0.887203867225*m.b151 >= -0.887203867225)
m.c1454 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b106 - 0.469370231236*m.b151 >= -0.469370231236)
m.c1455 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b121 - 0.469370231236*m.b151 >= -0.469370231236)
m.c1456 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b136 - 0.469370231236*m.b151 >= -0.469370231236)
m.c1457 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b151 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1458 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b151 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1459 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b151 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1460 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b151 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1461 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b151 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1462 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b46 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1463 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b55 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1464 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b64 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1465 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b73 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1466 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b82 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1467 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b91 - 0.887203867225*m.b166 >= -0.887203867225)
m.c1468 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b106 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1469 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b121 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1470 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b136 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1471 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b151 - 0.469370231236*m.b166 >= -0.469370231236)
m.c1472 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b166 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1473 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b166 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1474 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b166 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1475 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b166 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1476 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b46 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1477 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x199*m.x219 - 2*m.x200*m.x220
- 2.118573403024*m.b55 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1478 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b64 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1479 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b73 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1480 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b82 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1481 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b91 - 2.118573403024*m.b171 >= -2.118573403024)
m.c1482 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b106 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1483 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b121 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1484 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b136 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1485 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b151 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1486 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b166 - 1.436936830729*m.b171 >= -1.436936830729)
m.c1487 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b171 - 2.9321082756*m.b176 >= -2.9321082756)
m.c1488 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b171 - 2.9321082756*m.b181 >= -2.9321082756)
m.c1489 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b171 - 2.9321082756*m.b186 >= -2.9321082756)
m.c1490 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b46 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1491 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x199*m.x221 - 2*m.x200*m.x222
- 2.118573403024*m.b55 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1492 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b64 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1493 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b73 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1494 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b82 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1495 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b91 - 2.118573403024*m.b176 >= -2.118573403024)
m.c1496 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b106 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1497 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b121 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1498 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b136 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1499 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b151 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1500 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b166 - 1.436936830729*m.b176 >= -1.436936830729)
m.c1501 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b171 - 2.9321082756*m.b176 >= -2.9321082756)
m.c1502 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b176 - 2.9321082756*m.b181 >= -2.9321082756)
m.c1503 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b176 - 2.9321082756*m.b186 >= -2.9321082756)
m.c1504 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b46 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1505 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b55 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1506 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b64 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1507 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b73 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1508 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b82 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1509 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b91 - 2.118573403024*m.b181 >= -2.118573403024)
m.c1510 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b106 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1511 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b121 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1512 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b136 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1513 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b151 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1514 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b166 - 1.436936830729*m.b181 >= -1.436936830729)
m.c1515 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b171 - 2.9321082756*m.b181 >= -2.9321082756)
m.c1516 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b176 - 2.9321082756*m.b181 >= -2.9321082756)
m.c1517 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b181 - 2.9321082756*m.b186 >= -2.9321082756)
m.c1518 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b46 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1519 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b55 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1520 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b64 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1521 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b73 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1522 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b82 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1523 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b91 - 2.118573403024*m.b186 >= -2.118573403024)
m.c1524 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b106 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1525 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b121 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1526 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b136 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1527 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b151 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1528 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b166 - 1.436936830729*m.b186 >= -1.436936830729)
m.c1529 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b171 - 2.9321082756*m.b186 >= -2.9321082756)
m.c1530 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b176 - 2.9321082756*m.b186 >= -2.9321082756)
m.c1531 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b181 - 2.9321082756*m.b186 >= -2.9321082756)
m.c1532 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b47 - 1.436939228176*m.b56 >= -1.436939228176)
m.c1533 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b47 - 1.436939228176*m.b65 >= -1.436939228176)
m.c1534 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b47 - 1.436939228176*m.b74 >= -1.436939228176)
m.c1535 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b47 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1536 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b47 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1537 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b47 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1538 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b47 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1539 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b47 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1540 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b47 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1541 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b47 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1542 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b47 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1543 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x197*m.x221 - 2*m.x198*m.x222
- 2.118573403024*m.b47 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1544 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b47 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1545 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x197*m.x225 - 2*m.x198*m.x226
- 2.118573403024*m.b47 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1546 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b47 - 1.436939228176*m.b56 >= -1.436939228176)
m.c1547 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b56 - 1.436939228176*m.b65 >= -1.436939228176)
m.c1548 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b56 - 1.436939228176*m.b74 >= -1.436939228176)
m.c1549 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b56 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1550 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b56 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1551 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b56 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1552 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b56 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1553 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b56 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1554 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b56 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1555 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b56 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1556 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b56 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1557 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x199*m.x221 - 2*m.x200*m.x222
- 2.118573403024*m.b56 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1558 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b56 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1559 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b56 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1560 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b47 - 1.436939228176*m.b65 >= -1.436939228176)
m.c1561 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b56 - 1.436939228176*m.b65 >= -1.436939228176)
m.c1562 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b65 - 1.436939228176*m.b74 >= -1.436939228176)
m.c1563 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b65 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1564 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b65 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1565 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b65 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1566 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b65 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1567 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b65 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1568 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b65 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1569 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b65 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1570 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b65 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1571 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b65 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1572 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b65 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1573 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b65 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1574 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b47 - 1.436939228176*m.b74 >= -1.436939228176)
m.c1575 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b56 - 1.436939228176*m.b74 >= -1.436939228176)
m.c1576 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b65 - 1.436939228176*m.b74 >= -1.436939228176)
m.c1577 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b74 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1578 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b74 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1579 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b74 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1580 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b74 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1581 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b74 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1582 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b74 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1583 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b74 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1584 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b74 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1585 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b74 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1586 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b74 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1587 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b74 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1588 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b47 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1589 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b56 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1590 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b65 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1591 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b74 - 1.436939228176*m.b83 >= -1.436939228176)
m.c1592 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b83 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1593 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b83 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1594 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b83 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1595 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b83 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1596 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b83 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1597 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b83 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1598 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b83 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1599 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b83 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1600 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b83 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1601 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b83 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1602 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b47 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1603 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b56 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1604 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b65 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1605 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b74 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1606 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b83 - 1.436939228176*m.b92 >= -1.436939228176)
m.c1607 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b92 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1608 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b92 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1609 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b92 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1610 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b92 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1611 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b92 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1612 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b92 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1613 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b92 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1614 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b92 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1615 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b92 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1616 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b47 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1617 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b56 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1618 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b65 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1619 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b74 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1620 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b83 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1621 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b92 - 0.887203867225*m.b107 >= -0.887203867225)
m.c1622 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b107 - 0.469370231236*m.b122 >= -0.469370231236)
m.c1623 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b107 - 0.469370231236*m.b137 >= -0.469370231236)
m.c1624 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b107 - 0.469370231236*m.b152 >= -0.469370231236)
m.c1625 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b107 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1626 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b107 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1627 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b107 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1628 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b107 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1629 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b107 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1630 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b47 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1631 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b56 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1632 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b65 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1633 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b74 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1634 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b83 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1635 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b92 - 0.887203867225*m.b122 >= -0.887203867225)
m.c1636 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b107 - 0.469370231236*m.b122 >= -0.469370231236)
m.c1637 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b122 - 0.469370231236*m.b137 >= -0.469370231236)
m.c1638 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b122 - 0.469370231236*m.b152 >= -0.469370231236)
m.c1639 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b122 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1640 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b122 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1641 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b122 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1642 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b122 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1643 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b122 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1644 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b47 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1645 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x200*m.x214 - 2*m.x199*m.x213
- 0.887203867225*m.b56 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1646 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b65 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1647 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b74 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1648 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b83 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1649 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b92 - 0.887203867225*m.b137 >= -0.887203867225)
m.c1650 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b107 - 0.469370231236*m.b137 >= -0.469370231236)
m.c1651 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b122 - 0.469370231236*m.b137 >= -0.469370231236)
m.c1652 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b137 - 0.469370231236*m.b152 >= -0.469370231236)
m.c1653 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b137 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1654 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b137 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1655 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b137 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1656 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b137 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1657 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b137 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1658 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b47 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1659 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b56 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1660 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b65 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1661 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b74 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1662 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b83 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1663 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b92 - 0.887203867225*m.b152 >= -0.887203867225)
m.c1664 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b107 - 0.469370231236*m.b152 >= -0.469370231236)
m.c1665 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b122 - 0.469370231236*m.b152 >= -0.469370231236)
m.c1666 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b137 - 0.469370231236*m.b152 >= -0.469370231236)
m.c1667 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b152 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1668 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b152 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1669 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b152 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1670 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b152 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1671 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b152 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1672 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b47 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1673 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b56 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1674 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b65 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1675 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b74 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1676 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b83 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1677 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b92 - 0.887203867225*m.b167 >= -0.887203867225)
m.c1678 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b107 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1679 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b122 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1680 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b137 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1681 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b152 - 0.469370231236*m.b167 >= -0.469370231236)
m.c1682 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b167 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1683 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b167 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1684 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b167 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1685 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b167 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1686 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b47 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1687 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b56 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1688 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b65 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1689 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b74 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1690 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b83 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1691 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b92 - 2.118573403024*m.b172 >= -2.118573403024)
m.c1692 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b107 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1693 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b122 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1694 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b137 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1695 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b152 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1696 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b167 - 1.436936830729*m.b172 >= -1.436936830729)
m.c1697 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b172 - 2.9321082756*m.b177 >= -2.9321082756)
m.c1698 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b172 - 2.9321082756*m.b182 >= -2.9321082756)
m.c1699 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b172 - 2.9321082756*m.b187 >= -2.9321082756)
m.c1700 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b47 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1701 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b56 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1702 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b65 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1703 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x204*m.x222 - 2*m.x203*m.x221
- 2.118573403024*m.b74 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1704 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b83 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1705 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b92 - 2.118573403024*m.b177 >= -2.118573403024)
m.c1706 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b107 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1707 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b122 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1708 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b137 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1709 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b152 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1710 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b167 - 1.436936830729*m.b177 >= -1.436936830729)
m.c1711 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b172 - 2.9321082756*m.b177 >= -2.9321082756)
m.c1712 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b177 - 2.9321082756*m.b182 >= -2.9321082756)
m.c1713 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b177 - 2.9321082756*m.b187 >= -2.9321082756)
m.c1714 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b47 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1715 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b56 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1716 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b65 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1717 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b74 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1718 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b83 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1719 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b92 - 2.118573403024*m.b182 >= -2.118573403024)
m.c1720 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b107 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1721 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b122 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1722 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b137 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1723 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b152 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1724 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b167 - 1.436936830729*m.b182 >= -1.436936830729)
m.c1725 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b172 - 2.9321082756*m.b182 >= -2.9321082756)
m.c1726 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b177 - 2.9321082756*m.b182 >= -2.9321082756)
m.c1727 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b182 - 2.9321082756*m.b187 >= -2.9321082756)
m.c1728 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b47 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1729 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b56 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1730 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b65 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1731 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b74 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1732 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b83 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1733 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b92 - 2.118573403024*m.b187 >= -2.118573403024)
m.c1734 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b107 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1735 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b122 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1736 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b137 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1737 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b152 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1738 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b167 - 1.436936830729*m.b187 >= -1.436936830729)
m.c1739 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b172 - 2.9321082756*m.b187 >= -2.9321082756)
m.c1740 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b177 - 2.9321082756*m.b187 >= -2.9321082756)
m.c1741 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b182 - 2.9321082756*m.b187 >= -2.9321082756)
m.c1742 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b48 - 1.436939228176*m.b57 >= -1.436939228176)
m.c1743 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b48 - 1.436939228176*m.b66 >= -1.436939228176)
m.c1744 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b48 - 1.436939228176*m.b75 >= -1.436939228176)
m.c1745 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b48 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1746 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b48 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1747 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b48 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1748 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b48 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1749 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b48 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1750 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b48 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1751 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b48 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1752 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b48 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1753 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x197*m.x221 - 2*m.x198*m.x222
- 2.118573403024*m.b48 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1754 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b48 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1755 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b48 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1756 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b48 - 1.436939228176*m.b57 >= -1.436939228176)
m.c1757 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b57 - 1.436939228176*m.b66 >= -1.436939228176)
m.c1758 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b57 - 1.436939228176*m.b75 >= -1.436939228176)
m.c1759 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b57 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1760 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x200*m.x208 - 2*m.x199*m.x207
- 1.436939228176*m.b57 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1761 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b57 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1762 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b57 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1763 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b57 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1764 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b57 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1765 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x200*m.x218 - 2*m.x199*m.x217
- 0.887203867225*m.b57 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1766 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b57 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1767 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b57 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1768 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b57 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1769 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b57 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1770 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b48 - 1.436939228176*m.b66 >= -1.436939228176)
m.c1771 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b57 - 1.436939228176*m.b66 >= -1.436939228176)
m.c1772 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b66 - 1.436939228176*m.b75 >= -1.436939228176)
m.c1773 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b66 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1774 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b66 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1775 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b66 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1776 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b66 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1777 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b66 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1778 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b66 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1779 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b66 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1780 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b66 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1781 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b66 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1782 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b66 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1783 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b66 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1784 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b48 - 1.436939228176*m.b75 >= -1.436939228176)
m.c1785 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b57 - 1.436939228176*m.b75 >= -1.436939228176)
m.c1786 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b66 - 1.436939228176*m.b75 >= -1.436939228176)
m.c1787 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b75 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1788 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b75 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1789 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b75 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1790 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b75 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1791 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b75 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1792 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b75 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1793 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b75 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1794 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b75 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1795 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b75 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1796 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b75 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1797 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b75 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1798 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b48 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1799 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b57 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1800 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b66 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1801 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b75 - 1.436939228176*m.b84 >= -1.436939228176)
m.c1802 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b84 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1803 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b84 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1804 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b84 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1805 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b84 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1806 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b84 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1807 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b84 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1808 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b84 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1809 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b84 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1810 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b84 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1811 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b84 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1812 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b48 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1813 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b57 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1814 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b66 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1815 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b75 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1816 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b84 - 1.436939228176*m.b93 >= -1.436939228176)
m.c1817 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b93 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1818 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b93 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1819 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b93 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1820 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b93 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1821 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b93 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1822 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b93 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1823 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b93 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1824 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b93 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1825 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b93 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1826 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b48 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1827 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b57 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1828 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b66 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1829 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b75 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1830 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b84 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1831 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b93 - 0.887203867225*m.b108 >= -0.887203867225)
m.c1832 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b108 - 0.469370231236*m.b123 >= -0.469370231236)
m.c1833 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b108 - 0.469370231236*m.b138 >= -0.469370231236)
m.c1834 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b108 - 0.469370231236*m.b153 >= -0.469370231236)
m.c1835 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b108 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1836 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b108 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1837 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b108 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1838 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b108 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1839 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b108 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1840 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b48 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1841 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b57 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1842 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b66 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1843 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b75 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1844 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b84 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1845 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b93 - 0.887203867225*m.b123 >= -0.887203867225)
m.c1846 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b108 - 0.469370231236*m.b123 >= -0.469370231236)
m.c1847 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b123 - 0.469370231236*m.b138 >= -0.469370231236)
m.c1848 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b123 - 0.469370231236*m.b153 >= -0.469370231236)
m.c1849 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b123 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1850 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b123 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1851 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b123 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1852 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b123 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1853 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b123 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1854 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b48 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1855 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b57 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1856 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b66 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1857 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b75 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1858 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b84 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1859 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b93 - 0.887203867225*m.b138 >= -0.887203867225)
m.c1860 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b108 - 0.469370231236*m.b138 >= -0.469370231236)
m.c1861 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b123 - 0.469370231236*m.b138 >= -0.469370231236)
m.c1862 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b138 - 0.469370231236*m.b153 >= -0.469370231236)
m.c1863 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b138 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1864 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b138 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1865 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b138 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1866 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b138 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1867 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b138 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1868 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b48 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1869 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b57 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1870 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b66 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1871 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b75 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1872 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b84 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1873 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b93 - 0.887203867225*m.b153 >= -0.887203867225)
m.c1874 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b108 - 0.469370231236*m.b153 >= -0.469370231236)
m.c1875 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b123 - 0.469370231236*m.b153 >= -0.469370231236)
m.c1876 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b138 - 0.469370231236*m.b153 >= -0.469370231236)
m.c1877 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b153 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1878 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b153 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1879 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b153 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1880 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b153 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1881 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b153 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1882 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b48 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1883 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b57 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1884 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b66 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1885 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b75 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1886 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b84 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1887 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b93 - 0.887203867225*m.b168 >= -0.887203867225)
m.c1888 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b108 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1889 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b123 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1890 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b138 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1891 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b153 - 0.469370231236*m.b168 >= -0.469370231236)
m.c1892 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b168 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1893 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b168 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1894 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b168 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1895 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b168 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1896 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b48 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1897 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b57 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1898 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b66 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1899 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b75 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1900 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b84 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1901 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b93 - 2.118573403024*m.b173 >= -2.118573403024)
m.c1902 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b108 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1903 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b123 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1904 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b138 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1905 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b153 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1906 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b168 - 1.436936830729*m.b173 >= -1.436936830729)
m.c1907 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b173 - 2.9321082756*m.b178 >= -2.9321082756)
m.c1908 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b173 - 2.9321082756*m.b183 >= -2.9321082756)
m.c1909 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b173 - 2.9321082756*m.b188 >= -2.9321082756)
m.c1910 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b48 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1911 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b57 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1912 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b66 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1913 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b75 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1914 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b84 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1915 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b93 - 2.118573403024*m.b178 >= -2.118573403024)
m.c1916 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b108 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1917 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b123 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1918 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b138 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1919 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b153 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1920 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b168 - 1.436936830729*m.b178 >= -1.436936830729)
m.c1921 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b173 - 2.9321082756*m.b178 >= -2.9321082756)
m.c1922 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b178 - 2.9321082756*m.b183 >= -2.9321082756)
m.c1923 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b178 - 2.9321082756*m.b188 >= -2.9321082756)
m.c1924 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b48 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1925 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b57 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1926 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b66 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1927 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b75 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1928 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b84 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1929 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b93 - 2.118573403024*m.b183 >= -2.118573403024)
m.c1930 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b108 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1931 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b123 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1932 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b138 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1933 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b153 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1934 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b168 - 1.436936830729*m.b183 >= -1.436936830729)
m.c1935 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b173 - 2.9321082756*m.b183 >= -2.9321082756)
m.c1936 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b178 - 2.9321082756*m.b183 >= -2.9321082756)
m.c1937 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b183 - 2.9321082756*m.b188 >= -2.9321082756)
m.c1938 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b48 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1939 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b57 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1940 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b66 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1941 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b75 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1942 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b84 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1943 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b93 - 2.118573403024*m.b188 >= -2.118573403024)
m.c1944 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b108 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1945 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b123 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1946 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b138 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1947 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b153 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1948 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b168 - 1.436936830729*m.b188 >= -1.436936830729)
m.c1949 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b173 - 2.9321082756*m.b188 >= -2.9321082756)
m.c1950 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b178 - 2.9321082756*m.b188 >= -2.9321082756)
m.c1951 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b183 - 2.9321082756*m.b188 >= -2.9321082756)
m.c1952 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b49 - 1.436939228176*m.b58 >= -1.436939228176)
m.c1953 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b49 - 1.436939228176*m.b67 >= -1.436939228176)
m.c1954 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b49 - 1.436939228176*m.b76 >= -1.436939228176)
m.c1955 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b49 - 1.436939228176*m.b85 >= -1.436939228176)
m.c1956 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b49 - 1.436939228176*m.b94 >= -1.436939228176)
m.c1957 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b49 - 0.887203867225*m.b109 >= -0.887203867225)
m.c1958 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b49 - 0.887203867225*m.b124 >= -0.887203867225)
m.c1959 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b49 - 0.887203867225*m.b139 >= -0.887203867225)
m.c1960 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b49 - 0.887203867225*m.b154 >= -0.887203867225)
m.c1961 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b49 - 0.887203867225*m.b169 >= -0.887203867225)
m.c1962 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b49 - 2.118573403024*m.b174 >= -2.118573403024)
m.c1963 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x197*m.x221 - 2*m.x198*m.x222
- 2.118573403024*m.b49 - 2.118573403024*m.b179 >= -2.118573403024)
m.c1964 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b49 - 2.118573403024*m.b184 >= -2.118573403024)
m.c1965 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b49 - 2.118573403024*m.b189 >= -2.118573403024)
m.c1966 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x198*m.x228 - 2*m.x197*m.x227
- 4.509770398884*m.b49 - 4.509770398884*m.b191 >= -4.509770398884)
m.c1967 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x198*m.x230 - 2*m.x197*m.x229
- 4.509770398884*m.b49 - 4.509770398884*m.b193 >= -4.509770398884)
m.c1968 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b49 - 4.509770398884*m.b195 >= -4.509770398884)
m.c1969 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b49 - 1.436939228176*m.b58 >= -1.436939228176)
m.c1970 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b58 - 1.436939228176*m.b67 >= -1.436939228176)
m.c1971 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b58 - 1.436939228176*m.b76 >= -1.436939228176)
m.c1972 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b58 - 1.436939228176*m.b85 >= -1.436939228176)
m.c1973 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b58 - 1.436939228176*m.b94 >= -1.436939228176)
m.c1974 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b58 - 0.887203867225*m.b109 >= -0.887203867225)
m.c1975 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b58 - 0.887203867225*m.b124 >= -0.887203867225)
m.c1976 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b58 - 0.887203867225*m.b139 >= -0.887203867225)
m.c1977 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b58 - 0.887203867225*m.b154 >= -0.887203867225)
m.c1978 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b58 - 0.887203867225*m.b169 >= -0.887203867225)
m.c1979 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b58 - 2.118573403024*m.b174 >= -2.118573403024)
m.c1980 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x199*m.x221 - 2*m.x200*m.x222
- 2.118573403024*m.b58 - 2.118573403024*m.b179 >= -2.118573403024)
m.c1981 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b58 - 2.118573403024*m.b184 >= -2.118573403024)
m.c1982 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b58 - 2.118573403024*m.b189 >= -2.118573403024)
m.c1983 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b58 - 4.509770398884*m.b191 >= -4.509770398884)
m.c1984 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b58 - 4.509770398884*m.b193 >= -4.509770398884)
m.c1985 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b58 - 4.509770398884*m.b195 >= -4.509770398884)
m.c1986 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b49 - 1.436939228176*m.b67 >= -1.436939228176)
m.c1987 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b58 - 1.436939228176*m.b67 >= -1.436939228176)
m.c1988 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b67 - 1.436939228176*m.b76 >= -1.436939228176)
m.c1989 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b67 - 1.436939228176*m.b85 >= -1.436939228176)
m.c1990 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b67 - 1.436939228176*m.b94 >= -1.436939228176)
m.c1991 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b67 - 0.887203867225*m.b109 >= -0.887203867225)
m.c1992 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b67 - 0.887203867225*m.b124 >= -0.887203867225)
m.c1993 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b67 - 0.887203867225*m.b139 >= -0.887203867225)
m.c1994 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b67 - 0.887203867225*m.b154 >= -0.887203867225)
m.c1995 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b67 - 0.887203867225*m.b169 >= -0.887203867225)
m.c1996 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b67 - 2.118573403024*m.b174 >= -2.118573403024)
m.c1997 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b67 - 2.118573403024*m.b179 >= -2.118573403024)
m.c1998 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b67 - 2.118573403024*m.b184 >= -2.118573403024)
m.c1999 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b67 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2000 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b67 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2001 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x201*m.x229 - 2*m.x202*m.x230
- 4.509770398884*m.b67 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2002 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b67 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2003 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b49 - 1.436939228176*m.b76 >= -1.436939228176)
m.c2004 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b58 - 1.436939228176*m.b76 >= -1.436939228176)
m.c2005 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b67 - 1.436939228176*m.b76 >= -1.436939228176)
m.c2006 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b76 - 1.436939228176*m.b85 >= -1.436939228176)
m.c2007 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b76 - 1.436939228176*m.b94 >= -1.436939228176)
m.c2008 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b76 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2009 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b76 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2010 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b76 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2011 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b76 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2012 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b76 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2013 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b76 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2014 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b76 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2015 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x203*m.x223 - 2*m.x204*m.x224
- 2.118573403024*m.b76 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2016 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b76 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2017 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x203*m.x227 - 2*m.x204*m.x228
- 4.509770398884*m.b76 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2018 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b76 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2019 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b76 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2020 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b49 - 1.436939228176*m.b85 >= -1.436939228176)
m.c2021 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b58 - 1.436939228176*m.b85 >= -1.436939228176)
m.c2022 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b67 - 1.436939228176*m.b85 >= -1.436939228176)
m.c2023 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b76 - 1.436939228176*m.b85 >= -1.436939228176)
m.c2024 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b85 - 1.436939228176*m.b94 >= -1.436939228176)
m.c2025 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b85 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2026 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b85 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2027 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b85 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2028 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b85 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2029 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b85 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2030 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b85 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2031 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b85 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2032 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b85 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2033 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b85 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2034 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b85 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2035 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b85 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2036 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b85 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2037 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b49 - 1.436939228176*m.b94 >= -1.436939228176)
m.c2038 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b58 - 1.436939228176*m.b94 >= -1.436939228176)
m.c2039 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b67 - 1.436939228176*m.b94 >= -1.436939228176)
m.c2040 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b76 - 1.436939228176*m.b94 >= -1.436939228176)
m.c2041 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b85 - 1.436939228176*m.b94 >= -1.436939228176)
m.c2042 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b94 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2043 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b94 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2044 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b94 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2045 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b94 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2046 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b94 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2047 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b94 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2048 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b94 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2049 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b94 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2050 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b94 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2051 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b94 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2052 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b94 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2053 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b94 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2054 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b49 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2055 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b58 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2056 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b67 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2057 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b76 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2058 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b85 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2059 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b94 - 0.887203867225*m.b109 >= -0.887203867225)
m.c2060 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b109 - 0.469370231236*m.b124 >= -0.469370231236)
m.c2061 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b109 - 0.469370231236*m.b139 >= -0.469370231236)
m.c2062 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b109 - 0.469370231236*m.b154 >= -0.469370231236)
m.c2063 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b109 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2064 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b109 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2065 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b109 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2066 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b109 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2067 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b109 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2068 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b109 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2069 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b109 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2070 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b109 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2071 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b49 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2072 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b58 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2073 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b67 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2074 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b76 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2075 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b85 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2076 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b94 - 0.887203867225*m.b124 >= -0.887203867225)
m.c2077 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b109 - 0.469370231236*m.b124 >= -0.469370231236)
m.c2078 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b124 - 0.469370231236*m.b139 >= -0.469370231236)
m.c2079 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b124 - 0.469370231236*m.b154 >= -0.469370231236)
m.c2080 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b124 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2081 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b124 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2082 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b124 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2083 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b124 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2084 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b124 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2085 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b124 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2086 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b124 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2087 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b124 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2088 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b49 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2089 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b58 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2090 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b67 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2091 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b76 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2092 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b85 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2093 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b94 - 0.887203867225*m.b139 >= -0.887203867225)
m.c2094 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b109 - 0.469370231236*m.b139 >= -0.469370231236)
m.c2095 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b124 - 0.469370231236*m.b139 >= -0.469370231236)
m.c2096 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b139 - 0.469370231236*m.b154 >= -0.469370231236)
m.c2097 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b139 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2098 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b139 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2099 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b139 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2100 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b139 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2101 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b139 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2102 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b139 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2103 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b139 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2104 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b139 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2105 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b49 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2106 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b58 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2107 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b67 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2108 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b76 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2109 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b85 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2110 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b94 - 0.887203867225*m.b154 >= -0.887203867225)
m.c2111 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b109 - 0.469370231236*m.b154 >= -0.469370231236)
m.c2112 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b124 - 0.469370231236*m.b154 >= -0.469370231236)
m.c2113 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b139 - 0.469370231236*m.b154 >= -0.469370231236)
m.c2114 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b154 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2115 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b154 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2116 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b154 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2117 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b154 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2118 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b154 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2119 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b154 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2120 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b154 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2121 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b154 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2122 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b49 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2123 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b58 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2124 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b67 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2125 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b76 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2126 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b85 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2127 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b94 - 0.887203867225*m.b169 >= -0.887203867225)
m.c2128 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b109 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2129 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b124 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2130 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b139 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2131 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b154 - 0.469370231236*m.b169 >= -0.469370231236)
m.c2132 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b169 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2133 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b169 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2134 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b169 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2135 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b169 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2136 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b169 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2137 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b169 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2138 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b169 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2139 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b49 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2140 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b58 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2141 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b67 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2142 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b76 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2143 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b85 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2144 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b94 - 2.118573403024*m.b174 >= -2.118573403024)
m.c2145 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b109 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2146 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b124 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2147 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b139 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2148 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b154 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2149 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b169 - 1.436936830729*m.b174 >= -1.436936830729)
m.c2150 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b174 - 2.9321082756*m.b179 >= -2.9321082756)
m.c2151 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b174 - 2.9321082756*m.b184 >= -2.9321082756)
m.c2152 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b174 - 2.9321082756*m.b189 >= -2.9321082756)
m.c2153 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b174 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2154 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b174 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2155 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b174 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2156 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b49 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2157 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b58 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2158 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b67 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2159 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b76 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2160 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b85 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2161 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b94 - 2.118573403024*m.b179 >= -2.118573403024)
m.c2162 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b109 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2163 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b124 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2164 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b139 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2165 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b154 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2166 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b169 - 1.436936830729*m.b179 >= -1.436936830729)
m.c2167 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b174 - 2.9321082756*m.b179 >= -2.9321082756)
m.c2168 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b179 - 2.9321082756*m.b184 >= -2.9321082756)
m.c2169 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b179 - 2.9321082756*m.b189 >= -2.9321082756)
m.c2170 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b179 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2171 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b179 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2172 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b179 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2173 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b49 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2174 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b58 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2175 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b67 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2176 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x203*m.x223 - 2*m.x204*m.x224
- 2.118573403024*m.b76 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2177 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b85 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2178 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b94 - 2.118573403024*m.b184 >= -2.118573403024)
m.c2179 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b109 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2180 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b124 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2181 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b139 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2182 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b154 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2183 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b169 - 1.436936830729*m.b184 >= -1.436936830729)
m.c2184 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b174 - 2.9321082756*m.b184 >= -2.9321082756)
m.c2185 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b179 - 2.9321082756*m.b184 >= -2.9321082756)
m.c2186 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b184 - 2.9321082756*m.b189 >= -2.9321082756)
m.c2187 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b184 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2188 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b184 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2189 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b184 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2190 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b49 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2191 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b58 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2192 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b67 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2193 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b76 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2194 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b85 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2195 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b94 - 2.118573403024*m.b189 >= -2.118573403024)
m.c2196 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b109 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2197 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b124 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2198 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b139 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2199 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b154 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2200 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b169 - 1.436936830729*m.b189 >= -1.436936830729)
m.c2201 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b174 - 2.9321082756*m.b189 >= -2.9321082756)
m.c2202 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b179 - 2.9321082756*m.b189 >= -2.9321082756)
m.c2203 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b184 - 2.9321082756*m.b189 >= -2.9321082756)
m.c2204 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b189 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2205 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b189 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2206 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b189 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2207 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x197*m.x227 - 2*m.x198*m.x228
- 4.509770398884*m.b49 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2208 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b58 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2209 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b67 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2210 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x203*m.x227 - 2*m.x204*m.x228
- 4.509770398884*m.b76 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2211 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b85 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2212 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b94 - 4.509770398884*m.b191 >= -4.509770398884)
m.c2213 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b109 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2214 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b124 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2215 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b139 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2216 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b154 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2217 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b169 - 3.484990776969*m.b191 >= -3.484990776969)
m.c2218 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b174 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2219 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b179 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2220 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b184 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2221 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b189 - 5.6664469849*m.b191 >= -5.6664469849)
m.c2222 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b191 - 9.2934741904*m.b193 >= -9.2934741904)
m.c2223 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b191 - 9.2934741904*m.b195 >= -9.2934741904)
m.c2224 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x198*m.x230 - 2*m.x197*m.x229
- 4.509770398884*m.b49 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2225 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b58 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2226 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x202*m.x230 - 2*m.x201*m.x229
- 4.509770398884*m.b67 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2227 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b76 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2228 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b85 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2229 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b94 - 4.509770398884*m.b193 >= -4.509770398884)
m.c2230 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b109 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2231 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b124 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2232 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b139 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2233 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b154 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2234 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b169 - 3.484990776969*m.b193 >= -3.484990776969)
m.c2235 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b174 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2236 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b179 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2237 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b184 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2238 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b189 - 5.6664469849*m.b193 >= -5.6664469849)
m.c2239 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b191 - 9.2934741904*m.b193 >= -9.2934741904)
m.c2240 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b193 - 9.2934741904*m.b195 >= -9.2934741904)
m.c2241 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b49 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2242 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b58 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2243 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b67 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2244 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b76 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2245 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b85 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2246 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b94 - 4.509770398884*m.b195 >= -4.509770398884)
m.c2247 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b109 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2248 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b124 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2249 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b139 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2250 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b154 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2251 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b169 - 3.484990776969*m.b195 >= -3.484990776969)
m.c2252 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b174 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2253 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b179 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2254 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b184 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2255 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b189 - 5.6664469849*m.b195 >= -5.6664469849)
m.c2256 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b191 - 9.2934741904*m.b195 >= -9.2934741904)
m.c2257 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b193 - 9.2934741904*m.b195 >= -9.2934741904)
m.c2258 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b50 - 1.436939228176*m.b59 >= -1.436939228176)
m.c2259 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b50 - 1.436939228176*m.b68 >= -1.436939228176)
m.c2260 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b50 - 1.436939228176*m.b77 >= -1.436939228176)
m.c2261 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b50 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2262 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x197*m.x207 - 2*m.x198*m.x208
- 1.436939228176*m.b50 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2263 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b50 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2264 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b50 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2265 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b50 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2266 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b50 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2267 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b50 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2268 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b50 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2269 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x197*m.x221 - 2*m.x198*m.x222
- 2.118573403024*m.b50 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2270 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b50 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2271 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x197*m.x225 - 2*m.x198*m.x226
- 2.118573403024*m.b50 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2272 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x198*m.x228 - 2*m.x197*m.x227
- 4.509770398884*m.b50 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2273 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x198*m.x230 - 2*m.x197*m.x229
- 4.509770398884*m.b50 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2274 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b50 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2275 = Constraint(expr=m.x197**2 + m.x198**2 + m.x199**2 + m.x200**2 - 2*m.x197*m.x199 - 2*m.x198*m.x200
- 1.436939228176*m.b50 - 1.436939228176*m.b59 >= -1.436939228176)
m.c2276 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b59 - 1.436939228176*m.b68 >= -1.436939228176)
m.c2277 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b59 - 1.436939228176*m.b77 >= -1.436939228176)
m.c2278 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b59 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2279 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b59 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2280 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b59 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2281 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b59 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2282 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b59 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2283 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b59 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2284 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b59 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2285 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b59 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2286 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b59 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2287 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b59 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2288 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
- 2.118573403024*m.b59 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2289 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b59 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2290 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b59 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2291 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b59 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2292 = Constraint(expr=m.x197**2 + m.x198**2 + m.x201**2 + m.x202**2 - 2*m.x197*m.x201 - 2*m.x198*m.x202
- 1.436939228176*m.b50 - 1.436939228176*m.b68 >= -1.436939228176)
m.c2293 = Constraint(expr=m.x199**2 + m.x200**2 + m.x201**2 + m.x202**2 - 2*m.x199*m.x201 - 2*m.x200*m.x202
- 1.436939228176*m.b59 - 1.436939228176*m.b68 >= -1.436939228176)
m.c2294 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b68 - 1.436939228176*m.b77 >= -1.436939228176)
m.c2295 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b68 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2296 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b68 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2297 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b68 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2298 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b68 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2299 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b68 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2300 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b68 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2301 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b68 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2302 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b68 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2303 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b68 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2304 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
- 2.118573403024*m.b68 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2305 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b68 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2306 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b68 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2307 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x201*m.x229 - 2*m.x202*m.x230
- 4.509770398884*m.b68 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2308 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b68 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2309 = Constraint(expr=m.x197**2 + m.x198**2 + m.x203**2 + m.x204**2 - 2*m.x197*m.x203 - 2*m.x198*m.x204
- 1.436939228176*m.b50 - 1.436939228176*m.b77 >= -1.436939228176)
m.c2310 = Constraint(expr=m.x199**2 + m.x200**2 + m.x203**2 + m.x204**2 - 2*m.x199*m.x203 - 2*m.x200*m.x204
- 1.436939228176*m.b59 - 1.436939228176*m.b77 >= -1.436939228176)
m.c2311 = Constraint(expr=m.x201**2 + m.x202**2 + m.x203**2 + m.x204**2 - 2*m.x201*m.x203 - 2*m.x202*m.x204
- 1.436939228176*m.b68 - 1.436939228176*m.b77 >= -1.436939228176)
m.c2312 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b77 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2313 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b77 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2314 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b77 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2315 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b77 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2316 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b77 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2317 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b77 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2318 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b77 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2319 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b77 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2320 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b77 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2321 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b77 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2322 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b77 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2323 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x204*m.x228 - 2*m.x203*m.x227
- 4.509770398884*m.b77 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2324 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b77 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2325 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b77 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2326 = Constraint(expr=m.x197**2 + m.x198**2 + m.x205**2 + m.x206**2 - 2*m.x197*m.x205 - 2*m.x198*m.x206
- 1.436939228176*m.b50 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2327 = Constraint(expr=m.x199**2 + m.x200**2 + m.x205**2 + m.x206**2 - 2*m.x200*m.x206 - 2*m.x199*m.x205
- 1.436939228176*m.b59 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2328 = Constraint(expr=m.x201**2 + m.x202**2 + m.x205**2 + m.x206**2 - 2*m.x202*m.x206 - 2*m.x201*m.x205
- 1.436939228176*m.b68 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2329 = Constraint(expr=m.x203**2 + m.x204**2 + m.x205**2 + m.x206**2 - 2*m.x204*m.x206 - 2*m.x203*m.x205
- 1.436939228176*m.b77 - 1.436939228176*m.b86 >= -1.436939228176)
m.c2330 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b86 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2331 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b86 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2332 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b86 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2333 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b86 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2334 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b86 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2335 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b86 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2336 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b86 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2337 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b86 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2338 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b86 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2339 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b86 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2340 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b86 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2341 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b86 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2342 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b86 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2343 = Constraint(expr=m.x197**2 + m.x198**2 + m.x207**2 + m.x208**2 - 2*m.x198*m.x208 - 2*m.x197*m.x207
- 1.436939228176*m.b50 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2344 = Constraint(expr=m.x199**2 + m.x200**2 + m.x207**2 + m.x208**2 - 2*m.x199*m.x207 - 2*m.x200*m.x208
- 1.436939228176*m.b59 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2345 = Constraint(expr=m.x201**2 + m.x202**2 + m.x207**2 + m.x208**2 - 2*m.x202*m.x208 - 2*m.x201*m.x207
- 1.436939228176*m.b68 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2346 = Constraint(expr=m.x203**2 + m.x204**2 + m.x207**2 + m.x208**2 - 2*m.x204*m.x208 - 2*m.x203*m.x207
- 1.436939228176*m.b77 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2347 = Constraint(expr=m.x205**2 + m.x206**2 + m.x207**2 + m.x208**2 - 2*m.x205*m.x207 - 2*m.x206*m.x208
- 1.436939228176*m.b86 - 1.436939228176*m.b95 >= -1.436939228176)
m.c2348 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b95 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2349 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b95 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2350 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b95 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2351 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b95 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2352 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b95 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2353 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b95 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2354 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b95 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2355 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b95 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2356 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b95 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2357 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b95 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2358 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b95 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2359 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b95 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2360 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x198*m.x210 - 2*m.x197*m.x209
- 0.887203867225*m.b50 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2361 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x200*m.x210 - 2*m.x199*m.x209
- 0.887203867225*m.b59 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2362 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x202*m.x210 - 2*m.x201*m.x209
- 0.887203867225*m.b68 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2363 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x204*m.x210 - 2*m.x203*m.x209
- 0.887203867225*m.b77 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2364 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
- 0.887203867225*m.b86 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2365 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
- 0.887203867225*m.b95 - 0.887203867225*m.b110 >= -0.887203867225)
m.c2366 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b110 - 0.469370231236*m.b125 >= -0.469370231236)
m.c2367 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b110 - 0.469370231236*m.b140 >= -0.469370231236)
m.c2368 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b110 - 0.469370231236*m.b155 >= -0.469370231236)
m.c2369 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b110 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2370 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b110 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2371 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b110 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2372 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b110 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2373 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b110 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2374 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b110 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2375 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b110 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2376 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b110 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2377 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x198*m.x212 - 2*m.x197*m.x211
- 0.887203867225*m.b50 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2378 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x200*m.x212 - 2*m.x199*m.x211
- 0.887203867225*m.b59 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2379 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x202*m.x212 - 2*m.x201*m.x211
- 0.887203867225*m.b68 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2380 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x204*m.x212 - 2*m.x203*m.x211
- 0.887203867225*m.b77 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2381 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
- 0.887203867225*m.b86 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2382 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
- 0.887203867225*m.b95 - 0.887203867225*m.b125 >= -0.887203867225)
m.c2383 = Constraint(expr=m.x209**2 + m.x210**2 + m.x211**2 + m.x212**2 - 2*m.x209*m.x211 - 2*m.x210*m.x212
- 0.469370231236*m.b110 - 0.469370231236*m.b125 >= -0.469370231236)
m.c2384 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b125 - 0.469370231236*m.b140 >= -0.469370231236)
m.c2385 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b125 - 0.469370231236*m.b155 >= -0.469370231236)
m.c2386 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b125 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2387 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b125 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2388 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b125 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2389 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b125 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2390 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b125 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2391 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b125 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2392 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b125 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2393 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b125 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2394 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x198*m.x214 - 2*m.x197*m.x213
- 0.887203867225*m.b50 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2395 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
- 0.887203867225*m.b59 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2396 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x202*m.x214 - 2*m.x201*m.x213
- 0.887203867225*m.b68 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2397 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
- 0.887203867225*m.b77 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2398 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
- 0.887203867225*m.b86 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2399 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
- 0.887203867225*m.b95 - 0.887203867225*m.b140 >= -0.887203867225)
m.c2400 = Constraint(expr=m.x209**2 + m.x210**2 + m.x213**2 + m.x214**2 - 2*m.x209*m.x213 - 2*m.x210*m.x214
- 0.469370231236*m.b110 - 0.469370231236*m.b140 >= -0.469370231236)
m.c2401 = Constraint(expr=m.x211**2 + m.x212**2 + m.x213**2 + m.x214**2 - 2*m.x211*m.x213 - 2*m.x212*m.x214
- 0.469370231236*m.b125 - 0.469370231236*m.b140 >= -0.469370231236)
m.c2402 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b140 - 0.469370231236*m.b155 >= -0.469370231236)
m.c2403 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b140 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2404 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b140 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2405 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b140 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2406 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b140 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2407 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b140 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2408 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b140 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2409 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b140 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2410 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b140 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2411 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
- 0.887203867225*m.b50 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2412 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x200*m.x216 - 2*m.x199*m.x215
- 0.887203867225*m.b59 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2413 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
- 0.887203867225*m.b68 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2414 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x204*m.x216 - 2*m.x203*m.x215
- 0.887203867225*m.b77 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2415 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
- 0.887203867225*m.b86 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2416 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
- 0.887203867225*m.b95 - 0.887203867225*m.b155 >= -0.887203867225)
m.c2417 = Constraint(expr=m.x209**2 + m.x210**2 + m.x215**2 + m.x216**2 - 2*m.x209*m.x215 - 2*m.x210*m.x216
- 0.469370231236*m.b110 - 0.469370231236*m.b155 >= -0.469370231236)
m.c2418 = Constraint(expr=m.x211**2 + m.x212**2 + m.x215**2 + m.x216**2 - 2*m.x211*m.x215 - 2*m.x212*m.x216
- 0.469370231236*m.b125 - 0.469370231236*m.b155 >= -0.469370231236)
m.c2419 = Constraint(expr=m.x213**2 + m.x214**2 + m.x215**2 + m.x216**2 - 2*m.x213*m.x215 - 2*m.x214*m.x216
- 0.469370231236*m.b140 - 0.469370231236*m.b155 >= -0.469370231236)
m.c2420 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b155 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2421 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b155 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2422 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b155 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2423 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b155 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2424 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b155 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2425 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b155 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2426 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b155 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2427 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b155 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2428 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x198*m.x218 - 2*m.x197*m.x217
- 0.887203867225*m.b50 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2429 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
- 0.887203867225*m.b59 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2430 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x202*m.x218 - 2*m.x201*m.x217
- 0.887203867225*m.b68 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2431 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
- 0.887203867225*m.b77 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2432 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
- 0.887203867225*m.b86 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2433 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
- 0.887203867225*m.b95 - 0.887203867225*m.b170 >= -0.887203867225)
m.c2434 = Constraint(expr=m.x209**2 + m.x210**2 + m.x217**2 + m.x218**2 - 2*m.x209*m.x217 - 2*m.x210*m.x218
- 0.469370231236*m.b110 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2435 = Constraint(expr=m.x211**2 + m.x212**2 + m.x217**2 + m.x218**2 - 2*m.x211*m.x217 - 2*m.x212*m.x218
- 0.469370231236*m.b125 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2436 = Constraint(expr=m.x213**2 + m.x214**2 + m.x217**2 + m.x218**2 - 2*m.x213*m.x217 - 2*m.x214*m.x218
- 0.469370231236*m.b140 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2437 = Constraint(expr=m.x215**2 + m.x216**2 + m.x217**2 + m.x218**2 - 2*m.x215*m.x217 - 2*m.x216*m.x218
- 0.469370231236*m.b155 - 0.469370231236*m.b170 >= -0.469370231236)
m.c2438 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b170 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2439 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b170 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2440 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b170 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2441 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b170 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2442 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b170 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2443 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b170 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2444 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b170 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2445 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
- 2.118573403024*m.b50 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2446 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x200*m.x220 - 2*m.x199*m.x219
- 2.118573403024*m.b59 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2447 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
- 2.118573403024*m.b68 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2448 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x204*m.x220 - 2*m.x203*m.x219
- 2.118573403024*m.b77 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2449 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
- 2.118573403024*m.b86 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2450 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
- 2.118573403024*m.b95 - 2.118573403024*m.b175 >= -2.118573403024)
m.c2451 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
- 1.436936830729*m.b110 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2452 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
- 1.436936830729*m.b125 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2453 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
- 1.436936830729*m.b140 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2454 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
- 1.436936830729*m.b155 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2455 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
- 1.436936830729*m.b170 - 1.436936830729*m.b175 >= -1.436936830729)
m.c2456 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b175 - 2.9321082756*m.b180 >= -2.9321082756)
m.c2457 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b175 - 2.9321082756*m.b185 >= -2.9321082756)
m.c2458 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b175 - 2.9321082756*m.b190 >= -2.9321082756)
m.c2459 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b175 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2460 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b175 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2461 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b175 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2462 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x198*m.x222 - 2*m.x197*m.x221
- 2.118573403024*m.b50 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2463 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x200*m.x222 - 2*m.x199*m.x221
- 2.118573403024*m.b59 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2464 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x202*m.x222 - 2*m.x201*m.x221
- 2.118573403024*m.b68 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2465 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
- 2.118573403024*m.b77 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2466 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
- 2.118573403024*m.b86 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2467 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
- 2.118573403024*m.b95 - 2.118573403024*m.b180 >= -2.118573403024)
m.c2468 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
- 1.436936830729*m.b110 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2469 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
- 1.436936830729*m.b125 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2470 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
- 1.436936830729*m.b140 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2471 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
- 1.436936830729*m.b155 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2472 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
- 1.436936830729*m.b170 - 1.436936830729*m.b180 >= -1.436936830729)
m.c2473 = Constraint(expr=m.x219**2 + m.x220**2 + m.x221**2 + m.x222**2 - 2*m.x219*m.x221 - 2*m.x220*m.x222
- 2.9321082756*m.b175 - 2.9321082756*m.b180 >= -2.9321082756)
m.c2474 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b180 - 2.9321082756*m.b185 >= -2.9321082756)
m.c2475 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b180 - 2.9321082756*m.b190 >= -2.9321082756)
m.c2476 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b180 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2477 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b180 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2478 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b180 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2479 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
- 2.118573403024*m.b50 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2480 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x200*m.x224 - 2*m.x199*m.x223
- 2.118573403024*m.b59 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2481 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x202*m.x224 - 2*m.x201*m.x223
- 2.118573403024*m.b68 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2482 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x204*m.x224 - 2*m.x203*m.x223
- 2.118573403024*m.b77 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2483 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
- 2.118573403024*m.b86 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2484 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
- 2.118573403024*m.b95 - 2.118573403024*m.b185 >= -2.118573403024)
m.c2485 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
- 1.436936830729*m.b110 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2486 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
- 1.436936830729*m.b125 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2487 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
- 1.436936830729*m.b140 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2488 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
- 1.436936830729*m.b155 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2489 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
- 1.436936830729*m.b170 - 1.436936830729*m.b185 >= -1.436936830729)
m.c2490 = Constraint(expr=m.x219**2 + m.x220**2 + m.x223**2 + m.x224**2 - 2*m.x219*m.x223 - 2*m.x220*m.x224
- 2.9321082756*m.b175 - 2.9321082756*m.b185 >= -2.9321082756)
m.c2491 = Constraint(expr=m.x221**2 + m.x222**2 + m.x223**2 + m.x224**2 - 2*m.x221*m.x223 - 2*m.x222*m.x224
- 2.9321082756*m.b180 - 2.9321082756*m.b185 >= -2.9321082756)
m.c2492 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b185 - 2.9321082756*m.b190 >= -2.9321082756)
m.c2493 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b185 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2494 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b185 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2495 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b185 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2496 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x198*m.x226 - 2*m.x197*m.x225
- 2.118573403024*m.b50 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2497 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x200*m.x226 - 2*m.x199*m.x225
- 2.118573403024*m.b59 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2498 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x202*m.x226 - 2*m.x201*m.x225
- 2.118573403024*m.b68 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2499 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
- 2.118573403024*m.b77 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2500 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
- 2.118573403024*m.b86 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2501 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
- 2.118573403024*m.b95 - 2.118573403024*m.b190 >= -2.118573403024)
m.c2502 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
- 1.436936830729*m.b110 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2503 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
- 1.436936830729*m.b125 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2504 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
- 1.436936830729*m.b140 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2505 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
- 1.436936830729*m.b155 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2506 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
- 1.436936830729*m.b170 - 1.436936830729*m.b190 >= -1.436936830729)
m.c2507 = Constraint(expr=m.x219**2 + m.x220**2 + m.x225**2 + m.x226**2 - 2*m.x219*m.x225 - 2*m.x220*m.x226
- 2.9321082756*m.b175 - 2.9321082756*m.b190 >= -2.9321082756)
m.c2508 = Constraint(expr=m.x221**2 + m.x222**2 + m.x225**2 + m.x226**2 - 2*m.x221*m.x225 - 2*m.x222*m.x226
- 2.9321082756*m.b180 - 2.9321082756*m.b190 >= -2.9321082756)
m.c2509 = Constraint(expr=m.x223**2 + m.x224**2 + m.x225**2 + m.x226**2 - 2*m.x223*m.x225 - 2*m.x224*m.x226
- 2.9321082756*m.b185 - 2.9321082756*m.b190 >= -2.9321082756)
m.c2510 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b190 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2511 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b190 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2512 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b190 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2513 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x197*m.x227 - 2*m.x198*m.x228
- 4.509770398884*m.b50 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2514 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x200*m.x228 - 2*m.x199*m.x227
- 4.509770398884*m.b59 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2515 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
- 4.509770398884*m.b68 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2516 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x203*m.x227 - 2*m.x204*m.x228
- 4.509770398884*m.b77 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2517 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
- 4.509770398884*m.b86 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2518 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
- 4.509770398884*m.b95 - 4.509770398884*m.b192 >= -4.509770398884)
m.c2519 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
- 3.484990776969*m.b110 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2520 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
- 3.484990776969*m.b125 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2521 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
- 3.484990776969*m.b140 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2522 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
- 3.484990776969*m.b155 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2523 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
- 3.484990776969*m.b170 - 3.484990776969*m.b192 >= -3.484990776969)
m.c2524 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
- 5.6664469849*m.b175 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2525 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
- 5.6664469849*m.b180 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2526 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
- 5.6664469849*m.b185 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2527 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
- 5.6664469849*m.b190 - 5.6664469849*m.b192 >= -5.6664469849)
m.c2528 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b192 - 9.2934741904*m.b194 >= -9.2934741904)
m.c2529 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b192 - 9.2934741904*m.b196 >= -9.2934741904)
m.c2530 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x197*m.x229 - 2*m.x198*m.x230
- 4.509770398884*m.b50 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2531 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
- 4.509770398884*m.b59 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2532 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x202*m.x230 - 2*m.x201*m.x229
- 4.509770398884*m.b68 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2533 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x204*m.x230 - 2*m.x203*m.x229
- 4.509770398884*m.b77 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2534 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
- 4.509770398884*m.b86 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2535 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
- 4.509770398884*m.b95 - 4.509770398884*m.b194 >= -4.509770398884)
m.c2536 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
- 3.484990776969*m.b110 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2537 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
- 3.484990776969*m.b125 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2538 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
- 3.484990776969*m.b140 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2539 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
- 3.484990776969*m.b155 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2540 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
- 3.484990776969*m.b170 - 3.484990776969*m.b194 >= -3.484990776969)
m.c2541 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
- 5.6664469849*m.b175 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2542 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
- 5.6664469849*m.b180 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2543 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
- 5.6664469849*m.b185 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2544 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
- 5.6664469849*m.b190 - 5.6664469849*m.b194 >= -5.6664469849)
m.c2545 = Constraint(expr=m.x227**2 + m.x228**2 + m.x229**2 + m.x230**2 - 2*m.x227*m.x229 - 2*m.x228*m.x230
- 9.2934741904*m.b192 - 9.2934741904*m.b194 >= -9.2934741904)
m.c2546 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b194 - 9.2934741904*m.b196 >= -9.2934741904)
m.c2547 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x198*m.x232 - 2*m.x197*m.x231
- 4.509770398884*m.b50 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2548 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x200*m.x232 - 2*m.x199*m.x231
- 4.509770398884*m.b59 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2549 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x202*m.x232 - 2*m.x201*m.x231
- 4.509770398884*m.b68 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2550 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x204*m.x232 - 2*m.x203*m.x231
- 4.509770398884*m.b77 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2551 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
- 4.509770398884*m.b86 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2552 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
- 4.509770398884*m.b95 - 4.509770398884*m.b196 >= -4.509770398884)
m.c2553 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
- 3.484990776969*m.b110 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2554 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
- 3.484990776969*m.b125 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2555 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
- 3.484990776969*m.b140 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2556 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
- 3.484990776969*m.b155 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2557 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
- 3.484990776969*m.b170 - 3.484990776969*m.b196 >= -3.484990776969)
m.c2558 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
- 5.6664469849*m.b175 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2559 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
- 5.6664469849*m.b180 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2560 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
- 5.6664469849*m.b185 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2561 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
- 5.6664469849*m.b190 - 5.6664469849*m.b196 >= -5.6664469849)
m.c2562 = Constraint(expr=m.x227**2 + m.x228**2 + m.x231**2 + m.x232**2 - 2*m.x227*m.x231 - 2*m.x228*m.x232
- 9.2934741904*m.b192 - 9.2934741904*m.b196 >= -9.2934741904)
m.c2563 = Constraint(expr=m.x229**2 + m.x230**2 + m.x231**2 + m.x232**2 - 2*m.x229*m.x231 - 2*m.x230*m.x232
- 9.2934741904*m.b194 - 9.2934741904*m.b196 >= -9.2934741904)
m.c2564 = Constraint(expr=m.x197**2 + m.x198**2 + m.x219**2 + m.x220**2 - 2*m.x197*m.x219 - 2*m.x198*m.x220
+ 146.015866806048*m.b42 <= 146.035526210992)
m.c2565 = Constraint(expr=m.x197**2 + m.x198**2 + m.x221**2 + m.x222**2 - 2*m.x197*m.x221 - 2*m.x198*m.x222
+ 146.015866806048*m.b43 <= 146.035526210992)
m.c2566 = Constraint(expr=m.x197**2 + m.x198**2 + m.x223**2 + m.x224**2 - 2*m.x197*m.x223 - 2*m.x198*m.x224
+ 146.015866806048*m.b44 <= 146.035526210992)
m.c2567 = Constraint(expr=m.x197**2 + m.x198**2 + m.x225**2 + m.x226**2 - 2*m.x197*m.x225 - 2*m.x198*m.x226
+ 146.015866806048*m.b45 <= 146.035526210992)
m.c2568 = Constraint(expr=m.x197**2 + m.x198**2 + m.x227**2 + m.x228**2 - 2*m.x197*m.x227 - 2*m.x198*m.x228
+ 124.074660797768*m.b46 <= 124.688044241112)
m.c2569 = Constraint(expr=m.x197**2 + m.x198**2 + m.x229**2 + m.x230**2 - 2*m.x197*m.x229 - 2*m.x198*m.x230
+ 124.074660797768*m.b47 <= 124.688044241112)
m.c2570 = Constraint(expr=m.x197**2 + m.x198**2 + m.x231**2 + m.x232**2 - 2*m.x197*m.x231 - 2*m.x198*m.x232
+ 124.074660797768*m.b48 <= 124.688044241112)
m.c2571 = Constraint(expr=m.x197**2 + m.x198**2 + m.x233**2 + m.x234**2 - 2*m.x197*m.x233 - 2*m.x198*m.x234
+ 111.557223492128*m.b49 <= 112.885590384432)
m.c2572 = Constraint(expr=m.x197**2 + m.x198**2 + m.x235**2 + m.x236**2 - 2*m.x197*m.x235 - 2*m.x198*m.x236
+ 111.557223492128*m.b50 <= 112.885590384432)
m.c2573 = Constraint(expr=m.x199**2 + m.x200**2 + m.x219**2 + m.x220**2 - 2*m.x199*m.x219 - 2*m.x200*m.x220
+ 146.015866806048*m.b51 <= 146.035526210992)
m.c2574 = Constraint(expr=m.x199**2 + m.x200**2 + m.x221**2 + m.x222**2 - 2*m.x199*m.x221 - 2*m.x200*m.x222
+ 146.015866806048*m.b52 <= 146.035526210992)
m.c2575 = Constraint(expr=m.x199**2 + m.x200**2 + m.x223**2 + m.x224**2 - 2*m.x199*m.x223 - 2*m.x200*m.x224
+ 146.015866806048*m.b53 <= 146.035526210992)
m.c2576 = Constraint(expr=m.x199**2 + m.x200**2 + m.x225**2 + m.x226**2 - 2*m.x199*m.x225 - 2*m.x200*m.x226
+ 146.015866806048*m.b54 <= 146.035526210992)
m.c2577 = Constraint(expr=m.x199**2 + m.x200**2 + m.x227**2 + m.x228**2 - 2*m.x199*m.x227 - 2*m.x200*m.x228
+ 124.074660797768*m.b55 <= 124.688044241112)
m.c2578 = Constraint(expr=m.x199**2 + m.x200**2 + m.x229**2 + m.x230**2 - 2*m.x199*m.x229 - 2*m.x200*m.x230
+ 124.074660797768*m.b56 <= 124.688044241112)
m.c2579 = Constraint(expr=m.x199**2 + m.x200**2 + m.x231**2 + m.x232**2 - 2*m.x199*m.x231 - 2*m.x200*m.x232
+ 124.074660797768*m.b57 <= 124.688044241112)
m.c2580 = Constraint(expr=m.x199**2 + m.x200**2 + m.x233**2 + m.x234**2 - 2*m.x199*m.x233 - 2*m.x200*m.x234
+ 111.557223492128*m.b58 <= 112.885590384432)
m.c2581 = Constraint(expr=m.x199**2 + m.x200**2 + m.x235**2 + m.x236**2 - 2*m.x199*m.x235 - 2*m.x200*m.x236
+ 111.557223492128*m.b59 <= 112.885590384432)
m.c2582 = Constraint(expr=m.x201**2 + m.x202**2 + m.x219**2 + m.x220**2 - 2*m.x201*m.x219 - 2*m.x202*m.x220
+ 146.015866806048*m.b60 <= 146.035526210992)
m.c2583 = Constraint(expr=m.x201**2 + m.x202**2 + m.x221**2 + m.x222**2 - 2*m.x201*m.x221 - 2*m.x202*m.x222
+ 146.015866806048*m.b61 <= 146.035526210992)
m.c2584 = Constraint(expr=m.x201**2 + m.x202**2 + m.x223**2 + m.x224**2 - 2*m.x201*m.x223 - 2*m.x202*m.x224
+ 146.015866806048*m.b62 <= 146.035526210992)
m.c2585 = Constraint(expr=m.x201**2 + m.x202**2 + m.x225**2 + m.x226**2 - 2*m.x201*m.x225 - 2*m.x202*m.x226
+ 146.015866806048*m.b63 <= 146.035526210992)
m.c2586 = Constraint(expr=m.x201**2 + m.x202**2 + m.x227**2 + m.x228**2 - 2*m.x201*m.x227 - 2*m.x202*m.x228
+ 124.074660797768*m.b64 <= 124.688044241112)
m.c2587 = Constraint(expr=m.x201**2 + m.x202**2 + m.x229**2 + m.x230**2 - 2*m.x201*m.x229 - 2*m.x202*m.x230
+ 124.074660797768*m.b65 <= 124.688044241112)
m.c2588 = Constraint(expr=m.x201**2 + m.x202**2 + m.x231**2 + m.x232**2 - 2*m.x201*m.x231 - 2*m.x202*m.x232
+ 124.074660797768*m.b66 <= 124.688044241112)
m.c2589 = Constraint(expr=m.x201**2 + m.x202**2 + m.x233**2 + m.x234**2 - 2*m.x201*m.x233 - 2*m.x202*m.x234
+ 111.557223492128*m.b67 <= 112.885590384432)
m.c2590 = Constraint(expr=m.x201**2 + m.x202**2 + m.x235**2 + m.x236**2 - 2*m.x201*m.x235 - 2*m.x202*m.x236
+ 111.557223492128*m.b68 <= 112.885590384432)
m.c2591 = Constraint(expr=m.x203**2 + m.x204**2 + m.x219**2 + m.x220**2 - 2*m.x203*m.x219 - 2*m.x204*m.x220
+ 146.015866806048*m.b69 <= 146.035526210992)
m.c2592 = Constraint(expr=m.x203**2 + m.x204**2 + m.x221**2 + m.x222**2 - 2*m.x203*m.x221 - 2*m.x204*m.x222
+ 146.015866806048*m.b70 <= 146.035526210992)
m.c2593 = Constraint(expr=m.x203**2 + m.x204**2 + m.x223**2 + m.x224**2 - 2*m.x203*m.x223 - 2*m.x204*m.x224
+ 146.015866806048*m.b71 <= 146.035526210992)
m.c2594 = Constraint(expr=m.x203**2 + m.x204**2 + m.x225**2 + m.x226**2 - 2*m.x203*m.x225 - 2*m.x204*m.x226
+ 146.015866806048*m.b72 <= 146.035526210992)
m.c2595 = Constraint(expr=m.x203**2 + m.x204**2 + m.x227**2 + m.x228**2 - 2*m.x203*m.x227 - 2*m.x204*m.x228
+ 124.074660797768*m.b73 <= 124.688044241112)
m.c2596 = Constraint(expr=m.x203**2 + m.x204**2 + m.x229**2 + m.x230**2 - 2*m.x203*m.x229 - 2*m.x204*m.x230
+ 124.074660797768*m.b74 <= 124.688044241112)
m.c2597 = Constraint(expr=m.x203**2 + m.x204**2 + m.x231**2 + m.x232**2 - 2*m.x203*m.x231 - 2*m.x204*m.x232
+ 124.074660797768*m.b75 <= 124.688044241112)
m.c2598 = Constraint(expr=m.x203**2 + m.x204**2 + m.x233**2 + m.x234**2 - 2*m.x203*m.x233 - 2*m.x204*m.x234
+ 111.557223492128*m.b76 <= 112.885590384432)
m.c2599 = Constraint(expr=m.x203**2 + m.x204**2 + m.x235**2 + m.x236**2 - 2*m.x203*m.x235 - 2*m.x204*m.x236
+ 111.557223492128*m.b77 <= 112.885590384432)
m.c2600 = Constraint(expr=m.x205**2 + m.x206**2 + m.x219**2 + m.x220**2 - 2*m.x205*m.x219 - 2*m.x206*m.x220
+ 146.015866806048*m.b78 <= 146.035526210992)
m.c2601 = Constraint(expr=m.x205**2 + m.x206**2 + m.x221**2 + m.x222**2 - 2*m.x205*m.x221 - 2*m.x206*m.x222
+ 146.015866806048*m.b79 <= 146.035526210992)
m.c2602 = Constraint(expr=m.x205**2 + m.x206**2 + m.x223**2 + m.x224**2 - 2*m.x205*m.x223 - 2*m.x206*m.x224
+ 146.015866806048*m.b80 <= 146.035526210992)
m.c2603 = Constraint(expr=m.x205**2 + m.x206**2 + m.x225**2 + m.x226**2 - 2*m.x205*m.x225 - 2*m.x206*m.x226
+ 146.015866806048*m.b81 <= 146.035526210992)
m.c2604 = Constraint(expr=m.x205**2 + m.x206**2 + m.x227**2 + m.x228**2 - 2*m.x205*m.x227 - 2*m.x206*m.x228
+ 124.074660797768*m.b82 <= 124.688044241112)
m.c2605 = Constraint(expr=m.x205**2 + m.x206**2 + m.x229**2 + m.x230**2 - 2*m.x205*m.x229 - 2*m.x206*m.x230
+ 124.074660797768*m.b83 <= 124.688044241112)
m.c2606 = Constraint(expr=m.x205**2 + m.x206**2 + m.x231**2 + m.x232**2 - 2*m.x205*m.x231 - 2*m.x206*m.x232
+ 124.074660797768*m.b84 <= 124.688044241112)
m.c2607 = Constraint(expr=m.x205**2 + m.x206**2 + m.x233**2 + m.x234**2 - 2*m.x205*m.x233 - 2*m.x206*m.x234
+ 111.557223492128*m.b85 <= 112.885590384432)
m.c2608 = Constraint(expr=m.x205**2 + m.x206**2 + m.x235**2 + m.x236**2 - 2*m.x205*m.x235 - 2*m.x206*m.x236
+ 111.557223492128*m.b86 <= 112.885590384432)
m.c2609 = Constraint(expr=m.x207**2 + m.x208**2 + m.x219**2 + m.x220**2 - 2*m.x207*m.x219 - 2*m.x208*m.x220
+ 146.015866806048*m.b87 <= 146.035526210992)
m.c2610 = Constraint(expr=m.x207**2 + m.x208**2 + m.x221**2 + m.x222**2 - 2*m.x207*m.x221 - 2*m.x208*m.x222
+ 146.015866806048*m.b88 <= 146.035526210992)
m.c2611 = Constraint(expr=m.x207**2 + m.x208**2 + m.x223**2 + m.x224**2 - 2*m.x207*m.x223 - 2*m.x208*m.x224
+ 146.015866806048*m.b89 <= 146.035526210992)
m.c2612 = Constraint(expr=m.x207**2 + m.x208**2 + m.x225**2 + m.x226**2 - 2*m.x207*m.x225 - 2*m.x208*m.x226
+ 146.015866806048*m.b90 <= 146.035526210992)
m.c2613 = Constraint(expr=m.x207**2 + m.x208**2 + m.x227**2 + m.x228**2 - 2*m.x207*m.x227 - 2*m.x208*m.x228
+ 124.074660797768*m.b91 <= 124.688044241112)
m.c2614 = Constraint(expr=m.x207**2 + m.x208**2 + m.x229**2 + m.x230**2 - 2*m.x207*m.x229 - 2*m.x208*m.x230
+ 124.074660797768*m.b92 <= 124.688044241112)
m.c2615 = Constraint(expr=m.x207**2 + m.x208**2 + m.x231**2 + m.x232**2 - 2*m.x207*m.x231 - 2*m.x208*m.x232
+ 124.074660797768*m.b93 <= 124.688044241112)
m.c2616 = Constraint(expr=m.x207**2 + m.x208**2 + m.x233**2 + m.x234**2 - 2*m.x207*m.x233 - 2*m.x208*m.x234
+ 111.557223492128*m.b94 <= 112.885590384432)
m.c2617 = Constraint(expr=m.x207**2 + m.x208**2 + m.x235**2 + m.x236**2 - 2*m.x207*m.x235 - 2*m.x208*m.x236
+ 111.557223492128*m.b95 <= 112.885590384432)
m.c2618 = Constraint(expr=m.x197**2 + m.x198**2 + m.x209**2 + m.x210**2 - 2*m.x197*m.x209 - 2*m.x198*m.x210
+ 164.09780773445*m.b96 <= 164.128395645686)
m.c2619 = Constraint(expr=m.x199**2 + m.x200**2 + m.x209**2 + m.x210**2 - 2*m.x199*m.x209 - 2*m.x200*m.x210
+ 164.09780773445*m.b97 <= 164.128395645686)
m.c2620 = Constraint(expr=m.x201**2 + m.x202**2 + m.x209**2 + m.x210**2 - 2*m.x201*m.x209 - 2*m.x202*m.x210
+ 164.09780773445*m.b98 <= 164.128395645686)
m.c2621 = Constraint(expr=m.x203**2 + m.x204**2 + m.x209**2 + m.x210**2 - 2*m.x203*m.x209 - 2*m.x204*m.x210
+ 164.09780773445*m.b99 <= 164.128395645686)
m.c2622 = Constraint(expr=m.x205**2 + m.x206**2 + m.x209**2 + m.x210**2 - 2*m.x205*m.x209 - 2*m.x206*m.x210
+ 164.09780773445*m.b100 <= 164.128395645686)
m.c2623 = Constraint(expr=m.x207**2 + m.x208**2 + m.x209**2 + m.x210**2 - 2*m.x207*m.x209 - 2*m.x208*m.x210
+ 164.09780773445*m.b101 <= 164.128395645686)
m.c2624 = Constraint(expr=m.x209**2 + m.x210**2 + m.x219**2 + m.x220**2 - 2*m.x209*m.x219 - 2*m.x210*m.x220
+ 154.924953661458*m.b102 <= 155.082579335899)
m.c2625 = Constraint(expr=m.x209**2 + m.x210**2 + m.x221**2 + m.x222**2 - 2*m.x209*m.x221 - 2*m.x210*m.x222
+ 154.924953661458*m.b103 <= 155.082579335899)
m.c2626 = Constraint(expr=m.x209**2 + m.x210**2 + m.x223**2 + m.x224**2 - 2*m.x209*m.x223 - 2*m.x210*m.x224
+ 154.924953661458*m.b104 <= 155.082579335899)
m.c2627 = Constraint(expr=m.x209**2 + m.x210**2 + m.x225**2 + m.x226**2 - 2*m.x209*m.x225 - 2*m.x210*m.x226
+ 154.924953661458*m.b105 <= 155.082579335899)
m.c2628 = Constraint(expr=m.x209**2 + m.x210**2 + m.x227**2 + m.x228**2 - 2*m.x209*m.x227 - 2*m.x210*m.x228
+ 132.297461553938*m.b106 <= 133.379055313947)
m.c2629 = Constraint(expr=m.x209**2 + m.x210**2 + m.x229**2 + m.x230**2 - 2*m.x209*m.x229 - 2*m.x210*m.x230
+ 132.297461553938*m.b107 <= 133.379055313947)
m.c2630 = Constraint(expr=m.x209**2 + m.x210**2 + m.x231**2 + m.x232**2 - 2*m.x209*m.x231 - 2*m.x210*m.x232
+ 132.297461553938*m.b108 <= 133.379055313947)
m.c2631 = Constraint(expr=m.x209**2 + m.x210**2 + m.x233**2 + m.x234**2 - 2*m.x209*m.x233 - 2*m.x210*m.x234
+ 119.361045500978*m.b109 <= 121.347332654427)
m.c2632 = Constraint(expr=m.x209**2 + m.x210**2 + m.x235**2 + m.x236**2 - 2*m.x209*m.x235 - 2*m.x210*m.x236
+ 119.361045500978*m.b110 <= 121.347332654427)
m.c2633 = Constraint(expr=m.x197**2 + m.x198**2 + m.x211**2 + m.x212**2 - 2*m.x197*m.x211 - 2*m.x198*m.x212
+ 164.09780773445*m.b111 <= 164.128395645686)
m.c2634 = Constraint(expr=m.x199**2 + m.x200**2 + m.x211**2 + m.x212**2 - 2*m.x199*m.x211 - 2*m.x200*m.x212
+ 164.09780773445*m.b112 <= 164.128395645686)
m.c2635 = Constraint(expr=m.x201**2 + m.x202**2 + m.x211**2 + m.x212**2 - 2*m.x201*m.x211 - 2*m.x202*m.x212
+ 164.09780773445*m.b113 <= 164.128395645686)
m.c2636 = Constraint(expr=m.x203**2 + m.x204**2 + m.x211**2 + m.x212**2 - 2*m.x203*m.x211 - 2*m.x204*m.x212
+ 164.09780773445*m.b114 <= 164.128395645686)
m.c2637 = Constraint(expr=m.x205**2 + m.x206**2 + m.x211**2 + m.x212**2 - 2*m.x205*m.x211 - 2*m.x206*m.x212
+ 164.09780773445*m.b115 <= 164.128395645686)
m.c2638 = Constraint(expr=m.x207**2 + m.x208**2 + m.x211**2 + m.x212**2 - 2*m.x207*m.x211 - 2*m.x208*m.x212
+ 164.09780773445*m.b116 <= 164.128395645686)
m.c2639 = Constraint(expr=m.x211**2 + m.x212**2 + m.x219**2 + m.x220**2 - 2*m.x211*m.x219 - 2*m.x212*m.x220
+ 154.924953661458*m.b117 <= 155.082579335899)
m.c2640 = Constraint(expr=m.x211**2 + m.x212**2 + m.x221**2 + m.x222**2 - 2*m.x211*m.x221 - 2*m.x212*m.x222
+ 154.924953661458*m.b118 <= 155.082579335899)
m.c2641 = Constraint(expr=m.x211**2 + m.x212**2 + m.x223**2 + m.x224**2 - 2*m.x211*m.x223 - 2*m.x212*m.x224
+ 154.924953661458*m.b119 <= 155.082579335899)
m.c2642 = Constraint(expr=m.x211**2 + m.x212**2 + m.x225**2 + m.x226**2 - 2*m.x211*m.x225 - 2*m.x212*m.x226
+ 154.924953661458*m.b120 <= 155.082579335899)
m.c2643 = Constraint(expr=m.x211**2 + m.x212**2 + m.x227**2 + m.x228**2 - 2*m.x211*m.x227 - 2*m.x212*m.x228
+ 132.297461553938*m.b121 <= 133.379055313947)
m.c2644 = Constraint(expr=m.x211**2 + m.x212**2 + m.x229**2 + m.x230**2 - 2*m.x211*m.x229 - 2*m.x212*m.x230
+ 132.297461553938*m.b122 <= 133.379055313947)
m.c2645 = Constraint(expr=m.x211**2 + m.x212**2 + m.x231**2 + m.x232**2 - 2*m.x211*m.x231 - 2*m.x212*m.x232
+ 132.297461553938*m.b123 <= 133.379055313947)
m.c2646 = Constraint(expr=m.x211**2 + m.x212**2 + m.x233**2 + m.x234**2 - 2*m.x211*m.x233 - 2*m.x212*m.x234
+ 119.361045500978*m.b124 <= 121.347332654427)
m.c2647 = Constraint(expr=m.x211**2 + m.x212**2 + m.x235**2 + m.x236**2 - 2*m.x211*m.x235 - 2*m.x212*m.x236
+ 119.361045500978*m.b125 <= 121.347332654427)
m.c2648 = Constraint(expr=m.x197**2 + m.x198**2 + m.x213**2 + m.x214**2 - 2*m.x197*m.x213 - 2*m.x198*m.x214
+ 164.09780773445*m.b126 <= 164.128395645686)
m.c2649 = Constraint(expr=m.x199**2 + m.x200**2 + m.x213**2 + m.x214**2 - 2*m.x199*m.x213 - 2*m.x200*m.x214
+ 164.09780773445*m.b127 <= 164.128395645686)
m.c2650 = Constraint(expr=m.x201**2 + m.x202**2 + m.x213**2 + m.x214**2 - 2*m.x201*m.x213 - 2*m.x202*m.x214
+ 164.09780773445*m.b128 <= 164.128395645686)
m.c2651 = Constraint(expr=m.x203**2 + m.x204**2 + m.x213**2 + m.x214**2 - 2*m.x203*m.x213 - 2*m.x204*m.x214
+ 164.09780773445*m.b129 <= 164.128395645686)
m.c2652 = Constraint(expr=m.x205**2 + m.x206**2 + m.x213**2 + m.x214**2 - 2*m.x205*m.x213 - 2*m.x206*m.x214
+ 164.09780773445*m.b130 <= 164.128395645686)
m.c2653 = Constraint(expr=m.x207**2 + m.x208**2 + m.x213**2 + m.x214**2 - 2*m.x207*m.x213 - 2*m.x208*m.x214
+ 164.09780773445*m.b131 <= 164.128395645686)
m.c2654 = Constraint(expr=m.x213**2 + m.x214**2 + m.x219**2 + m.x220**2 - 2*m.x213*m.x219 - 2*m.x214*m.x220
+ 154.924953661458*m.b132 <= 155.082579335899)
m.c2655 = Constraint(expr=m.x213**2 + m.x214**2 + m.x221**2 + m.x222**2 - 2*m.x213*m.x221 - 2*m.x214*m.x222
+ 154.924953661458*m.b133 <= 155.082579335899)
m.c2656 = Constraint(expr=m.x213**2 + m.x214**2 + m.x223**2 + m.x224**2 - 2*m.x213*m.x223 - 2*m.x214*m.x224
+ 154.924953661458*m.b134 <= 155.082579335899)
m.c2657 = Constraint(expr=m.x213**2 + m.x214**2 + m.x225**2 + m.x226**2 - 2*m.x213*m.x225 - 2*m.x214*m.x226
+ 154.924953661458*m.b135 <= 155.082579335899)
m.c2658 = Constraint(expr=m.x213**2 + m.x214**2 + m.x227**2 + m.x228**2 - 2*m.x213*m.x227 - 2*m.x214*m.x228
+ 132.297461553938*m.b136 <= 133.379055313947)
m.c2659 = Constraint(expr=m.x213**2 + m.x214**2 + m.x229**2 + m.x230**2 - 2*m.x213*m.x229 - 2*m.x214*m.x230
+ 132.297461553938*m.b137 <= 133.379055313947)
m.c2660 = Constraint(expr=m.x213**2 + m.x214**2 + m.x231**2 + m.x232**2 - 2*m.x213*m.x231 - 2*m.x214*m.x232
+ 132.297461553938*m.b138 <= 133.379055313947)
m.c2661 = Constraint(expr=m.x213**2 + m.x214**2 + m.x233**2 + m.x234**2 - 2*m.x213*m.x233 - 2*m.x214*m.x234
+ 119.361045500978*m.b139 <= 121.347332654427)
m.c2662 = Constraint(expr=m.x213**2 + m.x214**2 + m.x235**2 + m.x236**2 - 2*m.x213*m.x235 - 2*m.x214*m.x236
+ 119.361045500978*m.b140 <= 121.347332654427)
m.c2663 = Constraint(expr=m.x197**2 + m.x198**2 + m.x215**2 + m.x216**2 - 2*m.x197*m.x215 - 2*m.x198*m.x216
+ 164.09780773445*m.b141 <= 164.128395645686)
m.c2664 = Constraint(expr=m.x199**2 + m.x200**2 + m.x215**2 + m.x216**2 - 2*m.x199*m.x215 - 2*m.x200*m.x216
+ 164.09780773445*m.b142 <= 164.128395645686)
m.c2665 = Constraint(expr=m.x201**2 + m.x202**2 + m.x215**2 + m.x216**2 - 2*m.x201*m.x215 - 2*m.x202*m.x216
+ 164.09780773445*m.b143 <= 164.128395645686)
m.c2666 = Constraint(expr=m.x203**2 + m.x204**2 + m.x215**2 + m.x216**2 - 2*m.x203*m.x215 - 2*m.x204*m.x216
+ 164.09780773445*m.b144 <= 164.128395645686)
m.c2667 = Constraint(expr=m.x205**2 + m.x206**2 + m.x215**2 + m.x216**2 - 2*m.x205*m.x215 - 2*m.x206*m.x216
+ 164.09780773445*m.b145 <= 164.128395645686)
m.c2668 = Constraint(expr=m.x207**2 + m.x208**2 + m.x215**2 + m.x216**2 - 2*m.x207*m.x215 - 2*m.x208*m.x216
+ 164.09780773445*m.b146 <= 164.128395645686)
m.c2669 = Constraint(expr=m.x215**2 + m.x216**2 + m.x219**2 + m.x220**2 - 2*m.x215*m.x219 - 2*m.x216*m.x220
+ 154.924953661458*m.b147 <= 155.082579335899)
m.c2670 = Constraint(expr=m.x215**2 + m.x216**2 + m.x221**2 + m.x222**2 - 2*m.x215*m.x221 - 2*m.x216*m.x222
+ 154.924953661458*m.b148 <= 155.082579335899)
m.c2671 = Constraint(expr=m.x215**2 + m.x216**2 + m.x223**2 + m.x224**2 - 2*m.x215*m.x223 - 2*m.x216*m.x224
+ 154.924953661458*m.b149 <= 155.082579335899)
m.c2672 = Constraint(expr=m.x215**2 + m.x216**2 + m.x225**2 + m.x226**2 - 2*m.x215*m.x225 - 2*m.x216*m.x226
+ 154.924953661458*m.b150 <= 155.082579335899)
m.c2673 = Constraint(expr=m.x215**2 + m.x216**2 + m.x227**2 + m.x228**2 - 2*m.x215*m.x227 - 2*m.x216*m.x228
+ 132.297461553938*m.b151 <= 133.379055313947)
m.c2674 = Constraint(expr=m.x215**2 + m.x216**2 + m.x229**2 + m.x230**2 - 2*m.x215*m.x229 - 2*m.x216*m.x230
+ 132.297461553938*m.b152 <= 133.379055313947)
m.c2675 = Constraint(expr=m.x215**2 + m.x216**2 + m.x231**2 + m.x232**2 - 2*m.x215*m.x231 - 2*m.x216*m.x232
+ 132.297461553938*m.b153 <= 133.379055313947)
m.c2676 = Constraint(expr=m.x215**2 + m.x216**2 + m.x233**2 + m.x234**2 - 2*m.x215*m.x233 - 2*m.x216*m.x234
+ 119.361045500978*m.b154 <= 121.347332654427)
m.c2677 = Constraint(expr=m.x215**2 + m.x216**2 + m.x235**2 + m.x236**2 - 2*m.x215*m.x235 - 2*m.x216*m.x236
+ 119.361045500978*m.b155 <= 121.347332654427)
m.c2678 = Constraint(expr=m.x197**2 + m.x198**2 + m.x217**2 + m.x218**2 - 2*m.x197*m.x217 - 2*m.x198*m.x218
+ 164.09780773445*m.b156 <= 164.128395645686)
m.c2679 = Constraint(expr=m.x199**2 + m.x200**2 + m.x217**2 + m.x218**2 - 2*m.x199*m.x217 - 2*m.x200*m.x218
+ 164.09780773445*m.b157 <= 164.128395645686)
m.c2680 = Constraint(expr=m.x201**2 + m.x202**2 + m.x217**2 + m.x218**2 - 2*m.x201*m.x217 - 2*m.x202*m.x218
+ 164.09780773445*m.b158 <= 164.128395645686)
m.c2681 = Constraint(expr=m.x203**2 + m.x204**2 + m.x217**2 + m.x218**2 - 2*m.x203*m.x217 - 2*m.x204*m.x218
+ 164.09780773445*m.b159 <= 164.128395645686)
m.c2682 = Constraint(expr=m.x205**2 + m.x206**2 + m.x217**2 + m.x218**2 - 2*m.x205*m.x217 - 2*m.x206*m.x218
+ 164.09780773445*m.b160 <= 164.128395645686)
m.c2683 = Constraint(expr=m.x207**2 + m.x208**2 + m.x217**2 + m.x218**2 - 2*m.x207*m.x217 - 2*m.x208*m.x218
+ 164.09780773445*m.b161 <= 164.128395645686)
m.c2684 = Constraint(expr=m.x217**2 + m.x218**2 + m.x219**2 + m.x220**2 - 2*m.x217*m.x219 - 2*m.x218*m.x220
+ 154.924953661458*m.b162 <= 155.082579335899)
m.c2685 = Constraint(expr=m.x217**2 + m.x218**2 + m.x221**2 + m.x222**2 - 2*m.x217*m.x221 - 2*m.x218*m.x222
+ 154.924953661458*m.b163 <= 155.082579335899)
m.c2686 = Constraint(expr=m.x217**2 + m.x218**2 + m.x223**2 + m.x224**2 - 2*m.x217*m.x223 - 2*m.x218*m.x224
+ 154.924953661458*m.b164 <= 155.082579335899)
m.c2687 = Constraint(expr=m.x217**2 + m.x218**2 + m.x225**2 + m.x226**2 - 2*m.x217*m.x225 - 2*m.x218*m.x226
+ 154.924953661458*m.b165 <= 155.082579335899)
m.c2688 = Constraint(expr=m.x217**2 + m.x218**2 + m.x227**2 + m.x228**2 - 2*m.x217*m.x227 - 2*m.x218*m.x228
+ 132.297461553938*m.b166 <= 133.379055313947)
m.c2689 = Constraint(expr=m.x217**2 + m.x218**2 + m.x229**2 + m.x230**2 - 2*m.x217*m.x229 - 2*m.x218*m.x230
+ 132.297461553938*m.b167 <= 133.379055313947)
m.c2690 = Constraint(expr=m.x217**2 + m.x218**2 + m.x231**2 + m.x232**2 - 2*m.x217*m.x231 - 2*m.x218*m.x232
+ 132.297461553938*m.b168 <= 133.379055313947)
m.c2691 = Constraint(expr=m.x217**2 + m.x218**2 + m.x233**2 + m.x234**2 - 2*m.x217*m.x233 - 2*m.x218*m.x234
+ 119.361045500978*m.b169 <= 121.347332654427)
m.c2692 = Constraint(expr=m.x217**2 + m.x218**2 + m.x235**2 + m.x236**2 - 2*m.x217*m.x235 - 2*m.x218*m.x236
+ 119.361045500978*m.b170 <= 121.347332654427)
m.c2693 = Constraint(expr=m.x219**2 + m.x220**2 + m.x227**2 + m.x228**2 - 2*m.x219*m.x227 - 2*m.x220*m.x228
+ 116.1156939698*m.b171 <= 116.3927698742)
m.c2694 = Constraint(expr=m.x219**2 + m.x220**2 + m.x229**2 + m.x230**2 - 2*m.x219*m.x229 - 2*m.x220*m.x230
+ 116.1156939698*m.b172 <= 116.3927698742)
m.c2695 = Constraint(expr=m.x219**2 + m.x220**2 + m.x231**2 + m.x232**2 - 2*m.x219*m.x231 - 2*m.x220*m.x232
+ 116.1156939698*m.b173 <= 116.3927698742)
m.c2696 = Constraint(expr=m.x219**2 + m.x220**2 + m.x233**2 + m.x234**2 - 2*m.x219*m.x233 - 2*m.x220*m.x234
+ 104.01723378*m.b174 <= 104.8195839276)
m.c2697 = Constraint(expr=m.x219**2 + m.x220**2 + m.x235**2 + m.x236**2 - 2*m.x219*m.x235 - 2*m.x220*m.x236
+ 104.01723378*m.b175 <= 104.8195839276)
m.c2698 = Constraint(expr=m.x221**2 + m.x222**2 + m.x227**2 + m.x228**2 - 2*m.x221*m.x227 - 2*m.x222*m.x228
+ 116.1156939698*m.b176 <= 116.3927698742)
m.c2699 = Constraint(expr=m.x221**2 + m.x222**2 + m.x229**2 + m.x230**2 - 2*m.x221*m.x229 - 2*m.x222*m.x230
+ 116.1156939698*m.b177 <= 116.3927698742)
m.c2700 = Constraint(expr=m.x221**2 + m.x222**2 + m.x231**2 + m.x232**2 - 2*m.x221*m.x231 - 2*m.x222*m.x232
+ 116.1156939698*m.b178 <= 116.3927698742)
m.c2701 = Constraint(expr=m.x221**2 + m.x222**2 + m.x233**2 + m.x234**2 - 2*m.x221*m.x233 - 2*m.x222*m.x234
+ 104.01723378*m.b179 <= 104.8195839276)
m.c2702 = Constraint(expr=m.x221**2 + m.x222**2 + m.x235**2 + m.x236**2 - 2*m.x221*m.x235 - 2*m.x222*m.x236
+ 104.01723378*m.b180 <= 104.8195839276)
m.c2703 = Constraint(expr=m.x223**2 + m.x224**2 + m.x227**2 + m.x228**2 - 2*m.x223*m.x227 - 2*m.x224*m.x228
+ 116.1156939698*m.b181 <= 116.3927698742)
m.c2704 = Constraint(expr=m.x223**2 + m.x224**2 + m.x229**2 + m.x230**2 - 2*m.x223*m.x229 - 2*m.x224*m.x230
+ 116.1156939698*m.b182 <= 116.3927698742)
m.c2705 = Constraint(expr=m.x223**2 + m.x224**2 + m.x231**2 + m.x232**2 - 2*m.x223*m.x231 - 2*m.x224*m.x232
+ 116.1156939698*m.b183 <= 116.3927698742)
m.c2706 = Constraint(expr=m.x223**2 + m.x224**2 + m.x233**2 + m.x234**2 - 2*m.x223*m.x233 - 2*m.x224*m.x234
+ 104.01723378*m.b184 <= 104.8195839276)
m.c2707 = Constraint(expr=m.x223**2 + m.x224**2 + m.x235**2 + m.x236**2 - 2*m.x223*m.x235 - 2*m.x224*m.x236
+ 104.01723378*m.b185 <= 104.8195839276)
m.c2708 = Constraint(expr=m.x225**2 + m.x226**2 + m.x227**2 + m.x228**2 - 2*m.x225*m.x227 - 2*m.x226*m.x228
+ 116.1156939698*m.b186 <= 116.3927698742)
m.c2709 = Constraint(expr=m.x225**2 + m.x226**2 + m.x229**2 + m.x230**2 - 2*m.x225*m.x229 - 2*m.x226*m.x230
+ 116.1156939698*m.b187 <= 116.3927698742)
m.c2710 = Constraint(expr=m.x225**2 + m.x226**2 + m.x231**2 + m.x232**2 - 2*m.x225*m.x231 - 2*m.x226*m.x232
+ 116.1156939698*m.b188 <= 116.3927698742)
m.c2711 = Constraint(expr=m.x225**2 + m.x226**2 + m.x233**2 + m.x234**2 - 2*m.x225*m.x233 - 2*m.x226*m.x234
+ 104.01723378*m.b189 <= 104.8195839276)
m.c2712 = Constraint(expr=m.x225**2 + m.x226**2 + m.x235**2 + m.x236**2 - 2*m.x225*m.x235 - 2*m.x226*m.x236
+ 104.01723378*m.b190 <= 104.8195839276)
m.c2713 = Constraint(expr=m.x227**2 + m.x228**2 + m.x233**2 + m.x234**2 - 2*m.x227*m.x233 - 2*m.x228*m.x234
+ 85.6376636642*m.b191 <= 85.6894881867)
m.c2714 = Constraint(expr=m.x227**2 + m.x228**2 + m.x235**2 + m.x236**2 - 2*m.x227*m.x235 - 2*m.x228*m.x236
+ 85.6376636642*m.b192 <= 85.6894881867)
m.c2715 = Constraint(expr=m.x229**2 + m.x230**2 + m.x233**2 + m.x234**2 - 2*m.x229*m.x233 - 2*m.x230*m.x234
+ 85.6376636642*m.b193 <= 85.6894881867)
m.c2716 = Constraint(expr=m.x229**2 + m.x230**2 + m.x235**2 + m.x236**2 - 2*m.x229*m.x235 - 2*m.x230*m.x236
+ 85.6376636642*m.b194 <= 85.6894881867)
m.c2717 = Constraint(expr=m.x231**2 + m.x232**2 + m.x233**2 + m.x234**2 - 2*m.x231*m.x233 - 2*m.x232*m.x234
+ 85.6376636642*m.b195 <= 85.6894881867)
m.c2718 = Constraint(expr=m.x231**2 + m.x232**2 + m.x235**2 + m.x236**2 - 2*m.x231*m.x235 - 2*m.x232*m.x236
+ 85.6376636642*m.b196 <= 85.6894881867)
m.c2719 = Constraint(expr= m.b2 + m.b3 + m.b42 + m.b43 + m.b44 + m.b45 + m.b46 + m.b47 + m.b48 + m.b49 + m.b50 <= 1)
m.c2720 = Constraint(expr= m.b4 + m.b5 + m.b51 + m.b52 + m.b53 + m.b54 + m.b55 + m.b56 + m.b57 + m.b58 + m.b59 <= 1)
m.c2721 = Constraint(expr= m.b6 + m.b7 + m.b60 + m.b61 + m.b62 + m.b63 + m.b64 + m.b65 + m.b66 + m.b67 + m.b68 <= 1)
m.c2722 = Constraint(expr= m.b8 + m.b9 + m.b69 + m.b70 + m.b71 + m.b72 + m.b73 + m.b74 + m.b75 + m.b76 + m.b77 <= 1)
m.c2723 = Constraint(expr= m.b10 + m.b11 + m.b78 + m.b79 + m.b80 + m.b81 + m.b82 + m.b83 + m.b84 + m.b85 + m.b86 <= 1)
m.c2724 = Constraint(expr= m.b12 + m.b13 + m.b87 + m.b88 + m.b89 + m.b90 + m.b91 + m.b92 + m.b93 + m.b94 + m.b95 <= 1)
m.c2725 = Constraint(expr= m.b14 + m.b15 + m.b96 + m.b97 + m.b98 + m.b99 + m.b100 + m.b101 + m.b102 + m.b103 + m.b104
+ m.b105 + m.b106 + m.b107 + m.b108 + m.b109 + m.b110 <= 1)
m.c2726 = Constraint(expr= m.b16 + m.b17 + m.b111 + m.b112 + m.b113 + m.b114 + m.b115 + m.b116 + m.b117 + m.b118
+ m.b119 + m.b120 + m.b121 + m.b122 + m.b123 + m.b124 + m.b125 <= 1)
m.c2727 = Constraint(expr= m.b18 + m.b19 + m.b126 + m.b127 + m.b128 + m.b129 + m.b130 + m.b131 + m.b132 + m.b133
+ m.b134 + m.b135 + m.b136 + m.b137 + m.b138 + m.b139 + m.b140 <= 1)
m.c2728 = Constraint(expr= m.b20 + m.b21 + m.b141 + m.b142 + m.b143 + m.b144 + m.b145 + m.b146 + m.b147 + m.b148
+ m.b149 + m.b150 + m.b151 + m.b152 + m.b153 + m.b154 + m.b155 <= 1)
m.c2729 = Constraint(expr= m.b22 + m.b23 + m.b156 + m.b157 + m.b158 + m.b159 + m.b160 + m.b161 + m.b162 + m.b163
+ m.b164 + m.b165 + m.b166 + m.b167 + m.b168 + m.b169 + m.b170 <= 1)
m.c2730 = Constraint(expr= m.b24 + m.b25 + m.b171 + m.b172 + m.b173 + m.b174 + m.b175 <= 1)
m.c2731 = Constraint(expr= m.b26 + m.b27 + m.b176 + m.b177 + m.b178 + m.b179 + m.b180 <= 1)
m.c2732 = Constraint(expr= m.b28 + m.b29 + m.b181 + m.b182 + m.b183 + m.b184 + m.b185 <= 1)
m.c2733 = Constraint(expr= m.b30 + m.b31 + m.b186 + m.b187 + m.b188 + m.b189 + m.b190 <= 1)
m.c2734 = Constraint(expr= m.b32 + m.b33 + m.b191 + m.b192 <= 1)
m.c2735 = Constraint(expr= m.b34 + m.b35 + m.b193 + m.b194 <= 1)
m.c2736 = Constraint(expr= m.b36 + m.b37 + m.b195 + m.b196 <= 1)
m.c2737 = Constraint(expr= m.b38 + m.b39 <= 1)
m.c2738 = Constraint(expr= m.b40 + m.b41 <= 1)
m.c2739 = Constraint(expr= - m.b24 - m.b25 + m.b42 <= 0)
m.c2740 = Constraint(expr= - m.b26 - m.b27 + m.b43 <= 0)
m.c2741 = Constraint(expr= - m.b28 - m.b29 + m.b44 <= 0)
m.c2742 = Constraint(expr= - m.b30 - m.b31 + m.b45 <= 0)
m.c2743 = Constraint(expr= - m.b32 - m.b33 + m.b46 <= 0)
m.c2744 = Constraint(expr= - m.b34 - m.b35 + m.b47 <= 0)
m.c2745 = Constraint(expr= - m.b36 - m.b37 + m.b48 <= 0)
m.c2746 = Constraint(expr= - m.b38 - m.b39 + m.b49 <= 0)
m.c2747 = Constraint(expr= - m.b40 - m.b41 + m.b50 <= 0)
m.c2748 = Constraint(expr= - m.b24 - m.b25 + m.b51 <= 0)
m.c2749 = Constraint(expr= - m.b26 - m.b27 + m.b52 <= 0)
m.c2750 = Constraint(expr= - m.b28 - m.b29 + m.b53 <= 0)
m.c2751 = Constraint(expr= - m.b30 - m.b31 + m.b54 <= 0)
m.c2752 = Constraint(expr= - m.b32 - m.b33 + m.b55 <= 0)
m.c2753 = Constraint(expr= - m.b34 - m.b35 + m.b56 <= 0)
m.c2754 = Constraint(expr= - m.b36 - m.b37 + m.b57 <= 0)
m.c2755 = Constraint(expr= - m.b38 - m.b39 + m.b58 <= 0)
m.c2756 = Constraint(expr= - m.b40 - m.b41 + m.b59 <= 0)
m.c2757 = Constraint(expr= - m.b24 - m.b25 + m.b60 <= 0)
m.c2758 = Constraint(expr= - m.b26 - m.b27 + m.b61 <= 0)
m.c2759 = Constraint(expr= - m.b28 - m.b29 + m.b62 <= 0)
m.c2760 = Constraint(expr= - m.b30 - m.b31 + m.b63 <= 0)
m.c2761 = Constraint(expr= - m.b32 - m.b33 + m.b64 <= 0)
m.c2762 = Constraint(expr= - m.b34 - m.b35 + m.b65 <= 0)
m.c2763 = Constraint(expr= - m.b36 - m.b37 + m.b66 <= 0)
m.c2764 = Constraint(expr= - m.b38 - m.b39 + m.b67 <= 0)
m.c2765 = Constraint(expr= - m.b40 - m.b41 + m.b68 <= 0)
m.c2766 = Constraint(expr= - m.b24 - m.b25 + m.b69 <= 0)
m.c2767 = Constraint(expr= - m.b26 - m.b27 + m.b70 <= 0)
m.c2768 = Constraint(expr= - m.b28 - m.b29 + m.b71 <= 0)
m.c2769 = Constraint(expr= - m.b30 - m.b31 + m.b72 <= 0)
m.c2770 = Constraint(expr= - m.b32 - m.b33 + m.b73 <= 0)
m.c2771 = Constraint(expr= - m.b34 - m.b35 + m.b74 <= 0)
m.c2772 = Constraint(expr= - m.b36 - m.b37 + m.b75 <= 0)
m.c2773 = Constraint(expr= - m.b38 - m.b39 + m.b76 <= 0)
m.c2774 = Constraint(expr= - m.b40 - m.b41 + m.b77 <= 0)
m.c2775 = Constraint(expr= - m.b24 - m.b25 + m.b78 <= 0)
m.c2776 = Constraint(expr= - m.b26 - m.b27 + m.b79 <= 0)
m.c2777 = Constraint(expr= - m.b28 - m.b29 + m.b80 <= 0)
m.c2778 = Constraint(expr= - m.b30 - m.b31 + m.b81 <= 0)
m.c2779 = Constraint(expr= - m.b32 - m.b33 + m.b82 <= 0)
m.c2780 = Constraint(expr= - m.b34 - m.b35 + m.b83 <= 0)
m.c2781 = Constraint(expr= - m.b36 - m.b37 + m.b84 <= 0)
m.c2782 = Constraint(expr= - m.b38 - m.b39 + m.b85 <= 0)
m.c2783 = Constraint(expr= - m.b40 - m.b41 + m.b86 <= 0)
m.c2784 = Constraint(expr= - m.b24 - m.b25 + m.b87 <= 0)
m.c2785 = Constraint(expr= - m.b26 - m.b27 + m.b88 <= 0)
m.c2786 = Constraint(expr= - m.b28 - m.b29 + m.b89 <= 0)
m.c2787 = Constraint(expr= - m.b30 - m.b31 + m.b90 <= 0)
m.c2788 = Constraint(expr= - m.b32 - m.b33 + m.b91 <= 0)
m.c2789 = Constraint(expr= - m.b34 - m.b35 + m.b92 <= 0)
m.c2790 = Constraint(expr= - m.b36 - m.b37 + m.b93 <= 0)
m.c2791 = Constraint(expr= - m.b38 - m.b39 + m.b94 <= 0)
m.c2792 = Constraint(expr= - m.b40 - m.b41 + m.b95 <= 0)
m.c2793 = Constraint(expr= - m.b2 - m.b3 + m.b96 <= 0)
m.c2794 = Constraint(expr= - m.b4 - m.b5 + m.b97 <= 0)
m.c2795 = Constraint(expr= - m.b6 - m.b7 + m.b98 <= 0)
m.c2796 = Constraint(expr= - m.b8 - m.b9 + m.b99 <= 0)
m.c2797 = Constraint(expr= - m.b10 - m.b11 + m.b100 <= 0)
m.c2798 = Constraint(expr= - m.b12 - m.b13 + m.b101 <= 0)
m.c2799 = Constraint(expr= - m.b24 - m.b25 + m.b102 <= 0)
m.c2800 = Constraint(expr= - m.b26 - m.b27 + m.b103 <= 0)
m.c2801 = Constraint(expr= - m.b28 - m.b29 + m.b104 <= 0)
m.c2802 = Constraint(expr= - m.b30 - m.b31 + m.b105 <= 0)
m.c2803 = Constraint(expr= - m.b32 - m.b33 + m.b106 <= 0)
m.c2804 = Constraint(expr= - m.b34 - m.b35 + m.b107 <= 0)
m.c2805 = Constraint(expr= - m.b36 - m.b37 + m.b108 <= 0)
m.c2806 = Constraint(expr= - m.b38 - m.b39 + m.b109 <= 0)
m.c2807 = Constraint(expr= - m.b40 - m.b41 + m.b110 <= 0)
m.c2808 = Constraint(expr= - m.b2 - m.b3 + m.b111 <= 0)
m.c2809 = Constraint(expr= - m.b4 - m.b5 + m.b112 <= 0)
m.c2810 = Constraint(expr= - m.b6 - m.b7 + m.b113 <= 0)
m.c2811 = Constraint(expr= - m.b8 - m.b9 + m.b114 <= 0)
m.c2812 = Constraint(expr= - m.b10 - m.b11 + m.b115 <= 0)
m.c2813 = Constraint(expr= - m.b12 - m.b13 + m.b116 <= 0)
m.c2814 = Constraint(expr= - m.b24 - m.b25 + m.b117 <= 0)
m.c2815 = Constraint(expr= - m.b26 - m.b27 + m.b118 <= 0)
m.c2816 = Constraint(expr= - m.b28 - m.b29 + m.b119 <= 0)
m.c2817 = Constraint(expr= - m.b30 - m.b31 + m.b120 <= 0)
m.c2818 = Constraint(expr= - m.b32 - m.b33 + m.b121 <= 0)
m.c2819 = Constraint(expr= - m.b34 - m.b35 + m.b122 <= 0)
m.c2820 = Constraint(expr= - m.b36 - m.b37 + m.b123 <= 0)
m.c2821 = Constraint(expr= - m.b38 - m.b39 + m.b124 <= 0)
m.c2822 = Constraint(expr= - m.b40 - m.b41 + m.b125 <= 0)
m.c2823 = Constraint(expr= - m.b2 - m.b3 + m.b126 <= 0)
m.c2824 = Constraint(expr= - m.b4 - m.b5 + m.b127 <= 0)
m.c2825 = Constraint(expr= - m.b6 - m.b7 + m.b128 <= 0)
m.c2826 = Constraint(expr= - m.b8 - m.b9 + m.b129 <= 0)
m.c2827 = Constraint(expr= - m.b10 - m.b11 + m.b130 <= 0)
m.c2828 = Constraint(expr= - m.b12 - m.b13 + m.b131 <= 0)
m.c2829 = Constraint(expr= - m.b24 - m.b25 + m.b132 <= 0)
m.c2830 = Constraint(expr= - m.b26 - m.b27 + m.b133 <= 0)
m.c2831 = Constraint(expr= - m.b28 - m.b29 + m.b134 <= 0)
m.c2832 = Constraint(expr= - m.b30 - m.b31 + m.b135 <= 0)
m.c2833 = Constraint(expr= - m.b32 - m.b33 + m.b136 <= 0)
m.c2834 = Constraint(expr= - m.b34 - m.b35 + m.b137 <= 0)
m.c2835 = Constraint(expr= - m.b36 - m.b37 + m.b138 <= 0)
m.c2836 = Constraint(expr= - m.b38 - m.b39 + m.b139 <= 0)
m.c2837 = Constraint(expr= - m.b40 - m.b41 + m.b140 <= 0)
m.c2838 = Constraint(expr= - m.b2 - m.b3 + m.b141 <= 0)
m.c2839 = Constraint(expr= - m.b4 - m.b5 + m.b142 <= 0)
m.c2840 = Constraint(expr= - m.b6 - m.b7 + m.b143 <= 0)
m.c2841 = Constraint(expr= - m.b8 - m.b9 + m.b144 <= 0)
m.c2842 = Constraint(expr= - m.b10 - m.b11 + m.b145 <= 0)
m.c2843 = Constraint(expr= - m.b12 - m.b13 + m.b146 <= 0)
m.c2844 = Constraint(expr= - m.b24 - m.b25 + m.b147 <= 0)
m.c2845 = Constraint(expr= - m.b26 - m.b27 + m.b148 <= 0)
m.c2846 = Constraint(expr= - m.b28 - m.b29 + m.b149 <= 0)
m.c2847 = Constraint(expr= - m.b30 - m.b31 + m.b150 <= 0)
m.c2848 = Constraint(expr= - m.b32 - m.b33 + m.b151 <= 0)
m.c2849 = Constraint(expr= - m.b34 - m.b35 + m.b152 <= 0)
m.c2850 = Constraint(expr= - m.b36 - m.b37 + m.b153 <= 0)
m.c2851 = Constraint(expr= - m.b38 - m.b39 + m.b154 <= 0)
m.c2852 = Constraint(expr= - m.b40 - m.b41 + m.b155 <= 0)
m.c2853 = Constraint(expr= - m.b2 - m.b3 + m.b156 <= 0)
m.c2854 = Constraint(expr= - m.b4 - m.b5 + m.b157 <= 0)
m.c2855 = Constraint(expr= - m.b6 - m.b7 + m.b158 <= 0)
m.c2856 = Constraint(expr= - m.b8 - m.b9 + m.b159 <= 0)
m.c2857 = Constraint(expr= - m.b10 - m.b11 + m.b160 <= 0)
m.c2858 = Constraint(expr= - m.b12 - m.b13 + m.b161 <= 0)
m.c2859 = Constraint(expr= - m.b24 - m.b25 + m.b162 <= 0)
m.c2860 = Constraint(expr= - m.b26 - m.b27 + m.b163 <= 0)
m.c2861 = Constraint(expr= - m.b28 - m.b29 + m.b164 <= 0)
m.c2862 = Constraint(expr= - m.b30 - m.b31 + m.b165 <= 0)
m.c2863 = Constraint(expr= - m.b32 - m.b33 + m.b166 <= 0)
m.c2864 = Constraint(expr= - m.b34 - m.b35 + m.b167 <= 0)
m.c2865 = Constraint(expr= - m.b36 - m.b37 + m.b168 <= 0)
m.c2866 = Constraint(expr= - m.b38 - m.b39 + m.b169 <= 0)
m.c2867 = Constraint(expr= - m.b40 - m.b41 + m.b170 <= 0)
m.c2868 = Constraint(expr= - m.b32 - m.b33 + m.b171 <= 0)
m.c2869 = Constraint(expr= - m.b34 - m.b35 + m.b172 <= 0)
m.c2870 = Constraint(expr= - m.b36 - m.b37 + m.b173 <= 0)
m.c2871 = Constraint(expr= - m.b38 - m.b39 + m.b174 <= 0)
m.c2872 = Constraint(expr= - m.b40 - m.b41 + m.b175 <= 0)
m.c2873 = Constraint(expr= - m.b32 - m.b33 + m.b176 <= 0)
m.c2874 = Constraint(expr= - m.b34 - m.b35 + m.b177 <= 0)
m.c2875 = Constraint(expr= - m.b36 - m.b37 + m.b178 <= 0)
m.c2876 = Constraint(expr= - m.b38 - m.b39 + m.b179 <= 0)
m.c2877 = Constraint(expr= - m.b40 - m.b41 + m.b180 <= 0)
m.c2878 = Constraint(expr= - m.b32 - m.b33 + m.b181 <= 0)
m.c2879 = Constraint(expr= - m.b34 - m.b35 + m.b182 <= 0)
m.c2880 = Constraint(expr= - m.b36 - m.b37 + m.b183 <= 0)
m.c2881 = Constraint(expr= - m.b38 - m.b39 + m.b184 <= 0)
m.c2882 = Constraint(expr= - m.b40 - m.b41 + m.b185 <= 0)
m.c2883 = Constraint(expr= - m.b32 - m.b33 + m.b186 <= 0)
m.c2884 = Constraint(expr= - m.b34 - m.b35 + m.b187 <= 0)
m.c2885 = Constraint(expr= - m.b36 - m.b37 + m.b188 <= 0)
m.c2886 = Constraint(expr= - m.b38 - m.b39 + m.b189 <= 0)
m.c2887 = Constraint(expr= - m.b40 - m.b41 + m.b190 <= 0)
m.c2888 = Constraint(expr= - m.b38 - m.b39 + m.b191 <= 0)
m.c2889 = Constraint(expr= - m.b40 - m.b41 + m.b192 <= 0)
m.c2890 = Constraint(expr= - m.b38 - m.b39 + m.b193 <= 0)
m.c2891 = Constraint(expr= - m.b40 - m.b41 + m.b194 <= 0)
m.c2892 = Constraint(expr= - m.b38 - m.b39 + m.b195 <= 0)
m.c2893 = Constraint(expr= - m.b40 - m.b41 + m.b196 <= 0)
m.c2894 = Constraint(expr= m.x197 - m.x199 <= 0)
m.c2895 = Constraint(expr= m.x197 - m.x201 <= 0)
m.c2896 = Constraint(expr= m.x197 - m.x203 <= 0)
m.c2897 = Constraint(expr= m.x197 - m.x205 <= 0)
m.c2898 = Constraint(expr= m.x197 - m.x207 <= 0)
m.c2899 = Constraint(expr= m.x199 - m.x201 <= 0)
m.c2900 = Constraint(expr= m.x199 - m.x203 <= 0)
m.c2901 = Constraint(expr= m.x199 - m.x205 <= 0)
m.c2902 = Constraint(expr= m.x199 - m.x207 <= 0)
m.c2903 = Constraint(expr= m.x201 - m.x203 <= 0)
m.c2904 = Constraint(expr= m.x201 - m.x205 <= 0)
m.c2905 = Constraint(expr= m.x201 - m.x207 <= 0)
m.c2906 = Constraint(expr= m.x203 - m.x205 <= 0)
m.c2907 = Constraint(expr= m.x203 - m.x207 <= 0)
m.c2908 = Constraint(expr= m.x205 - m.x207 <= 0)
m.c2909 = Constraint(expr= m.x209 - m.x211 <= 0)
m.c2910 = Constraint(expr= m.x209 - m.x213 <= 0)
m.c2911 = Constraint(expr= m.x209 - m.x215 <= 0)
m.c2912 = Constraint(expr= m.x209 - m.x217 <= 0)
m.c2913 = Constraint(expr= m.x211 - m.x213 <= 0)
m.c2914 = Constraint(expr= m.x211 - m.x215 <= 0)
m.c2915 = Constraint(expr= m.x211 - m.x217 <= 0)
m.c2916 = Constraint(expr= m.x213 - m.x215 <= 0)
m.c2917 = Constraint(expr= m.x213 - m.x217 <= 0)
m.c2918 = Constraint(expr= m.x215 - m.x217 <= 0)
m.c2919 = Constraint(expr= m.x219 - m.x221 <= 0)
m.c2920 = Constraint(expr= m.x219 - m.x223 <= 0)
m.c2921 = Constraint(expr= m.x219 - m.x225 <= 0)
m.c2922 = Constraint(expr= m.x221 - m.x223 <= 0)
m.c2923 = Constraint(expr= m.x221 - m.x225 <= 0)
m.c2924 = Constraint(expr= m.x223 - m.x225 <= 0)
m.c2925 = Constraint(expr= m.x227 - m.x229 <= 0)
m.c2926 = Constraint(expr= m.x227 - m.x231 <= 0)
m.c2927 = Constraint(expr= m.x229 - m.x231 <= 0)
m.c2928 = Constraint(expr= m.x233 - m.x235 <= 0)
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0
| 8
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11cad2ba91b2572f78363c6cdda937a1df7b28a7
| 4,785
|
py
|
Python
|
src/discussion/migrations/0022_externalcomment_externalreply_externalthread.py
|
ResearchHub/ResearchHub-Backend-Open
|
d36dca33afae2d442690694bb2ab17180d84bcd3
|
[
"MIT"
] | 18
|
2021-05-20T13:20:16.000Z
|
2022-02-11T02:40:18.000Z
|
src/discussion/migrations/0022_externalcomment_externalreply_externalthread.py
|
ResearchHub/ResearchHub-Backend-Open
|
d36dca33afae2d442690694bb2ab17180d84bcd3
|
[
"MIT"
] | 109
|
2021-05-21T20:14:23.000Z
|
2022-03-31T20:56:10.000Z
|
src/discussion/migrations/0022_externalcomment_externalreply_externalthread.py
|
ResearchHub/ResearchHub-Backend-Open
|
d36dca33afae2d442690694bb2ab17180d84bcd3
|
[
"MIT"
] | 4
|
2021-05-17T13:47:53.000Z
|
2022-02-12T10:48:21.000Z
|
# Generated by Django 2.2.12 on 2020-05-29 21:05
from django.conf import settings
import django.contrib.postgres.fields.jsonb
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('discussion', '0021_auto_20200429_2210'),
]
operations = [
migrations.CreateModel(
name='ExternalThread',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_date', models.DateTimeField(auto_now_add=True, db_index=True)),
('updated_date', models.DateTimeField(auto_now=True)),
('was_edited', models.BooleanField(default=False, help_text='True if the comment text was edited after first being created.')),
('is_public', models.BooleanField(default=True, help_text='Hides the comment from the public.')),
('is_removed', models.BooleanField(default=False, help_text='Hides the comment because it is not allowed.')),
('ip_address', models.GenericIPAddressField(blank=True, null=True, unpack_ipv4=True)),
('text', django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)),
('plain_text', models.TextField(blank=True, default='')),
('source_id', models.CharField(max_length=64)),
('source', models.CharField(max_length=16)),
('username', models.CharField(max_length=32)),
('created_by', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='ExternalReply',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_date', models.DateTimeField(auto_now_add=True, db_index=True)),
('updated_date', models.DateTimeField(auto_now=True)),
('was_edited', models.BooleanField(default=False, help_text='True if the comment text was edited after first being created.')),
('is_public', models.BooleanField(default=True, help_text='Hides the comment from the public.')),
('is_removed', models.BooleanField(default=False, help_text='Hides the comment because it is not allowed.')),
('ip_address', models.GenericIPAddressField(blank=True, null=True, unpack_ipv4=True)),
('text', django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)),
('plain_text', models.TextField(blank=True, default='')),
('source_id', models.CharField(max_length=64)),
('source', models.CharField(max_length=16)),
('username', models.CharField(max_length=32)),
('created_by', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='ExternalComment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_date', models.DateTimeField(auto_now_add=True, db_index=True)),
('updated_date', models.DateTimeField(auto_now=True)),
('was_edited', models.BooleanField(default=False, help_text='True if the comment text was edited after first being created.')),
('is_public', models.BooleanField(default=True, help_text='Hides the comment from the public.')),
('is_removed', models.BooleanField(default=False, help_text='Hides the comment because it is not allowed.')),
('ip_address', models.GenericIPAddressField(blank=True, null=True, unpack_ipv4=True)),
('text', django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)),
('plain_text', models.TextField(blank=True, default='')),
('source_id', models.CharField(max_length=64)),
('source', models.CharField(max_length=16)),
('username', models.CharField(max_length=32)),
('created_by', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)),
],
options={
'abstract': False,
},
),
]
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0
| 8
|
eee01b8a3354351f9511f6bb6f55d685efc2d386
| 173
|
py
|
Python
|
0x0A-python-inheritance/0-lookup.py
|
johncoleman83/bootcampschool-higher_level_programming
|
a83c3b7092cfe893c87e495f8d8eec9228c9b808
|
[
"MIT"
] | null | null | null |
0x0A-python-inheritance/0-lookup.py
|
johncoleman83/bootcampschool-higher_level_programming
|
a83c3b7092cfe893c87e495f8d8eec9228c9b808
|
[
"MIT"
] | null | null | null |
0x0A-python-inheritance/0-lookup.py
|
johncoleman83/bootcampschool-higher_level_programming
|
a83c3b7092cfe893c87e495f8d8eec9228c9b808
|
[
"MIT"
] | 1
|
2020-09-25T17:54:36.000Z
|
2020-09-25T17:54:36.000Z
|
#!/usr/bin/python3
"""module to return all attributes and methods of object"""
def lookup(obj):
"""returns all attributes and methods of object"""
return dir(obj)
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| 59
| 0.693642
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|
0
| 7
|
e125833c1e0bf8dfb970cf7ef3a884ea74a66e04
| 22,391
|
py
|
Python
|
script/regrasp.py
|
HKUST-RML/Shallow_Depth_Insertion
|
c2559479285d69a514e81467c5582f6384fc5dc1
|
[
"MIT"
] | 3
|
2021-08-19T12:41:16.000Z
|
2021-09-09T09:51:50.000Z
|
script/regrasp.py
|
HKUST-RML/Shallow_Depth_Insertion
|
c2559479285d69a514e81467c5582f6384fc5dc1
|
[
"MIT"
] | null | null | null |
script/regrasp.py
|
HKUST-RML/Shallow_Depth_Insertion
|
c2559479285d69a514e81467c5582f6384fc5dc1
|
[
"MIT"
] | 1
|
2022-01-13T08:24:18.000Z
|
2022-01-13T08:24:18.000Z
|
#!/usr/bin/env python
import sys
import math
import time
import rospy
import copy
import numpy as np
import tf
import moveit_commander
import helper
import motion_primitives
import tilt
import yaml
import actionlib
import visualization
import dynamixel
import random
from robotiq_2f_gripper_msgs.msg import CommandRobotiqGripperFeedback, CommandRobotiqGripperResult, CommandRobotiqGripperAction, CommandRobotiqGripperGoal
from robotiq_2f_gripper_control.robotiq_2f_gripper_driver import Robotiq2FingerGripperDriver as Robotiq
moveit_commander.roscpp_initialize(sys.argv)
robot = moveit_commander.RobotCommander()
scene = moveit_commander.PlanningSceneInterface()
group = moveit_commander.MoveGroupCommander("manipulator")
def regrasp(axis, angle, velocity):
with open("/home/john/catkin_ws/src/shallow_depth_insertion/config/sdi_config.yaml", 'r') as stream:
try:
config = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
pose_target = group.get_current_pose().pose
pos_initial = [pose_target.position.x, pose_target.position.y, pose_target.position.z]
ori_initial = [pose_target.orientation.x, pose_target.orientation.y, pose_target.orientation.z, pose_target.orientation.w]
T_we = tf.TransformListener().fromTranslationRotation(pos_initial, ori_initial)
tcp2fingertip = config['tcp2fingertip']
contact_A_e = [tcp2fingertip, -config['object_thickness']/2, 0, 1] #TODO: depends on axis direction
contact_A_w = np.matmul(T_we, contact_A_e)
visualization.visualizer(contact_A_w[:3], "s", 0.01, 1) #DEBUG
# Interpolate orientation poses via quaternion slerp
q = helper.axis_angle2quaternion(axis, angle)
ori_target = tf.transformations.quaternion_multiply(q, ori_initial)
ori_waypoints = helper.slerp(ori_initial, ori_target, np.arange(1.0/angle , 1.0+1.0/angle, 1.0/angle))
theta_0 = config['theta_0']
waypoints = []
action_name = rospy.get_param('~action_name', 'command_robotiq_action')
robotiq_client = actionlib.SimpleActionClient(action_name, CommandRobotiqGripperAction)
for psi in range(1, angle+1):
# Calculate width
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
# Calculate position
if theta_0 + psi <= 90:
hori = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi)))
verti = math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi))) - math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi)))
else:
hori = -math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi-90)))
verti = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi-90)))
if axis[0] > 0:
pose_target.position.y = contact_A_w[1] + hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 1"
elif axis[0] < 0:
pose_target.position.y = contact_A_w[1] - hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 2"
elif axis[1] > 0:
pose_target.position.x = contact_A_w[0] - hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 3"
elif axis[1] < 0:
pose_target.position.x = contact_A_w[0] + hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 4"
pose_target.orientation.x = ori_waypoints[psi-1][0]
pose_target.orientation.y = ori_waypoints[psi-1][1]
pose_target.orientation.z = ori_waypoints[psi-1][2]
pose_target.orientation.w = ori_waypoints[psi-1][3]
waypoints.append(copy.deepcopy(pose_target))
(plan, fraction) = group.compute_cartesian_path(waypoints, 0.01, 0)
retimed_plan = group.retime_trajectory(robot.get_current_state(), plan, velocity)
group.execute(retimed_plan, wait=False)
opening_at_zero = config['max_opening']-2*config['finger_thickness']
psi = 0
while psi < angle:
pose = group.get_current_pose().pose
q_current = [pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w]
psi = 2*math.degrees(math.acos(np.dot(q_current, ori_initial)))
if psi > 100:
psi = -(psi-360)
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
Robotiq.goto(robotiq_client, pos=width+0.007, speed=config['gripper_speed'], force=config['gripper_force'], block=False) #0.006 for coin; 0.000 for book; 0.005 for poker
psi = round(psi, 2)
rospy.sleep(0.5)
return width
def palm_regrasp(axis, angle, velocity):
with open("/home/john/catkin_ws/src/shallow_depth_insertion/config/sdi_config.yaml", 'r') as stream:
try:
config = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
pose_target = group.get_current_pose().pose
pos_initial = [pose_target.position.x, pose_target.position.y, pose_target.position.z]
ori_initial = [pose_target.orientation.x, pose_target.orientation.y, pose_target.orientation.z, pose_target.orientation.w]
T_we = tf.TransformListener().fromTranslationRotation(pos_initial, ori_initial)
tcp2fingertip = config['tcp2fingertip']
contact_A_e = [tcp2fingertip, config['object_thickness']/2, 0, 1] #TODO: depends on axis direction
contact_A_w = np.matmul(T_we, contact_A_e)
visualization.visualizer(contact_A_w[:3], "s", 0.01, 1) #DEBUG
# Interpolate orientation poses via quaternion slerp
q = helper.axis_angle2quaternion(axis, angle)
ori_target = tf.transformations.quaternion_multiply(q, ori_initial)
ori_waypoints = helper.slerp(ori_initial, ori_target, np.arange(1.0/angle , 1.0+1.0/angle, 1.0/angle))
theta_0 = config['theta_0']
waypoints = []
action_name = rospy.get_param('~action_name', 'command_robotiq_action')
robotiq_client = actionlib.SimpleActionClient(action_name, CommandRobotiqGripperAction)
for psi in range(1, angle+1):
# Calculate width
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
# Calculate position
if theta_0 + psi <= 90:
hori = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi)))
verti = math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi))) - math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi)))
else:
hori = -math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi-90)))
verti = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi-90)))
'''
#Left Vertical Case
if axis[0] > 0:
pose_target.position.y = contact_A_w[1] - verti
pose_target.position.z = contact_A_w[2] + hori
#print "CASE 1"
#Right Vertical Case
elif axis[0] < 0:
pose_target.position.y = contact_A_w[1] - verti
pose_target.position.z = contact_A_w[2] - hori
#print "CASE 2"
'''
if axis[0] > 0:
pose_target.position.y = contact_A_w[1] - hori
pose_target.position.z = contact_A_w[2] - verti
#print "CASE 1"
#Normal Case
elif axis[0] < 0:
pose_target.position.y = contact_A_w[1] - hori #(- +) ( + + ) ( + -)
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 2"
elif axis[1] > 0:
pose_target.position.x = contact_A_w[0] - hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 3"
elif axis[1] < 0:
pose_target.position.x = contact_A_w[0] + hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 4"
pose_target.orientation.x = ori_waypoints[psi-1][0]
pose_target.orientation.y = ori_waypoints[psi-1][1]
pose_target.orientation.z = ori_waypoints[psi-1][2]
pose_target.orientation.w = ori_waypoints[psi-1][3]
waypoints.append(copy.deepcopy(pose_target))
(plan, fraction) = group.compute_cartesian_path(waypoints, 0.01, 0)
retimed_plan = group.retime_trajectory(robot.get_current_state(), plan, velocity)
group.execute(retimed_plan, wait=False)
opening_at_zero = config['max_opening']-2*config['finger_thickness']
psi = 0
while psi < angle:
pose = group.get_current_pose().pose
q_current = [pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w]
psi = 2*math.degrees(math.acos(np.dot(q_current, ori_initial)))
if psi > 100:
psi = -(psi-360)
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
palm_position = 128 + 1.2*(config['delta_0'] - a)*1000
#pos = int((opening_at_zero - width)/config['opening_per_count'])
Robotiq.goto(robotiq_client, pos=width+0.005, speed=config['gripper_speed'], force=config['gripper_force'], block=False)
print palm_position
dynamixel.set_length(palm_position)
psi = round(psi, 2)
rospy.sleep(0.5)
def inverted_palm_regrasp(axis, angle, velocity):
with open("/home/john/catkin_ws/src/shallow_depth_insertion/config/sdi_config.yaml", 'r') as stream:
try:
config = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
pose_target = group.get_current_pose().pose
pos_initial = [pose_target.position.x, pose_target.position.y, pose_target.position.z]
ori_initial = [pose_target.orientation.x, pose_target.orientation.y, pose_target.orientation.z, pose_target.orientation.w]
T_we = tf.TransformListener().fromTranslationRotation(pos_initial, ori_initial)
tcp2fingertip = config['tcp2fingertip']
contact_A_e = [tcp2fingertip, config['object_thickness']/2, 0, 1] #TODO: depends on axis direction
contact_A_w = np.matmul(T_we, contact_A_e)
visualization.visualizer(contact_A_w[:3], "s", 0.01, 1) #DEBUG
# Interpolate orientation poses via quaternion slerp
q = helper.axis_angle2quaternion(axis, angle)
ori_target = tf.transformations.quaternion_multiply(q, ori_initial)
ori_waypoints = helper.slerp(ori_initial, ori_target, np.arange(1.0/angle , 1.0+1.0/angle, 1.0/angle))
theta_0 = config['theta_0']
waypoints = []
action_name = rospy.get_param('~action_name', 'command_robotiq_action')
robotiq_client = actionlib.SimpleActionClient(action_name, CommandRobotiqGripperAction)
for psi in range(1, angle+1):
# Calculate width
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
# Calculate position
if theta_0 + psi <= 90:
hori = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi)))
verti = math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi))) - math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi)))
else:
hori = -math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi-90)))
verti = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi-90)))
if axis[0] > 0:
pose_target.position.y = contact_A_w[1] - hori # TODO: The only thing I changed for inverted palm regrasp is the (-) sign
pose_target.position.z = contact_A_w[2] - verti
#print "CASE 1"
elif axis[0] < 0:
pose_target.position.y = contact_A_w[1] + hori
pose_target.position.z = contact_A_w[2] - verti
#print "CASE 2"
elif axis[1] > 0:
pose_target.position.x = contact_A_w[0] - hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 3"
elif axis[1] < 0:
pose_target.position.x = contact_A_w[0] + hori
pose_target.position.z = contact_A_w[2] + verti
#print "CASE 4"
pose_target.orientation.x = ori_waypoints[psi-1][0]
pose_target.orientation.y = ori_waypoints[psi-1][1]
pose_target.orientation.z = ori_waypoints[psi-1][2]
pose_target.orientation.w = ori_waypoints[psi-1][3]
waypoints.append(copy.deepcopy(pose_target))
(plan, fraction) = group.compute_cartesian_path(waypoints, 0.01, 0)
retimed_plan = group.retime_trajectory(robot.get_current_state(), plan, velocity)
group.execute(retimed_plan, wait=False)
opening_at_zero = config['max_opening']-2*config['finger_thickness']
psi = 0
while psi < angle:
pose = group.get_current_pose().pose
q_current = [pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w]
psi = 2*math.degrees(math.acos(np.dot(q_current, ori_initial)))
if psi > 100:
psi = -(psi-360)
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
palm_position = 127 + (config['delta_0'] - a)*1000
#pos = int((opening_at_zero - width)/config['opening_per_count'])
Robotiq.goto(robotiq_client, pos=width+0.003, speed=config['gripper_speed'], force=config['gripper_force'], block=False)
dynamixel.set_length(palm_position+9)
psi = round(psi, 2)
def second_regrasp(axis, angle, pos, velocity):
with open("/home/john/catkin_ws/src/shallow_depth_insertion/config/sdi_config.yaml", 'r') as stream:
try:
config = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
tcp2fingertip = config['tcp2fingertip']
p = group.get_current_pose().pose
trans_tool0 = [p.position.x, p.position.y, p.position.z]
rot_tool0 = [p.orientation.x, p.orientation.y, p.orientation.z, p.orientation.w]
T_wg = tf.TransformerROS().fromTranslationRotation(trans_tool0, rot_tool0)
P_g_center = [tcp2fingertip+0.02, -pos/2.0, 0, 1]
P_w_center = np.matmul(T_wg, P_g_center)
center = [P_w_center[0], P_w_center[1], P_w_center[2]]
waypoints = tilt.tilt_no_wait(center, axis, int(angle), velocity)
#print waypoints
rospy.sleep(0.5)
action_name = rospy.get_param('~action_name', 'command_robotiq_action')
robotiq_client = actionlib.SimpleActionClient(action_name, CommandRobotiqGripperAction)
current_angle = 0
while current_angle < angle:
pose = group.get_current_pose().pose
q_current = [pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w]
current_angle = 2*math.degrees(math.acos(np.dot(q_current, rot_tool0)))
if current_angle > 100:
current_angle = -(psi-360)
Robotiq.goto(robotiq_client, pos=pos+0.00315+0.012*(current_angle/angle), speed=config['gripper_speed'], force=config['gripper_force'], block=False)
current_angle = round(current_angle, 2)
def active_regrasp(axis, angle, velocity, active_distance, psi_active_transition, active_distance_2):
with open("/home/john/catkin_ws/src/shallow_depth_insertion/config/sdi_config.yaml", 'r') as stream:
try:
config = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
pose_target = group.get_current_pose().pose
pos_initial = [pose_target.position.x, pose_target.position.y, pose_target.position.z]
ori_initial = [pose_target.orientation.x, pose_target.orientation.y, pose_target.orientation.z, pose_target.orientation.w]
T_we = tf.TransformListener().fromTranslationRotation(pos_initial, ori_initial)
tcp2fingertip = config['tcp2fingertip']
error = 0.00
contact_A_e = [tcp2fingertip+random.uniform(-error, error), -config['object_thickness']/2+random.uniform(-error, error), 0, 1] #TODO: depends on axis direction
contact_A_w = np.matmul(T_we, contact_A_e)
visualization.visualizer(contact_A_w[:3], "s", 0.01, 1) #DEBUG
# Interpolate orientation poses via quaternion slerp
q = helper.axis_angle2quaternion(axis, angle)
ori_target = tf.transformations.quaternion_multiply(q, ori_initial)
ori_waypoints = helper.slerp(ori_initial, ori_target, np.arange(1.0/angle , 1.0+1.0/angle, 1.0/angle))
theta_0 = config['theta_0']
waypoints = []
action_name = rospy.get_param('~action_name', 'command_robotiq_action')
robotiq_client = actionlib.SimpleActionClient(action_name, CommandRobotiqGripperAction)
for psi in range(1, angle+1):
# Calculate width
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
# Calculate position
if theta_0 + psi <= 90:
hori = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi)))
verti = math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi))) - math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi)))
else:
hori = -math.fabs(tcp2fingertip*math.sin(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.cos(math.radians(theta_0+psi-90)))
verti = math.fabs(tcp2fingertip*math.cos(math.radians(theta_0 + psi-90))) + math.fabs((width/2.0)*math.sin(math.radians(theta_0+psi-90)))
if axis[0] > 0:
pose_target.position.y = contact_A_w[1] + hori
pose_target.position.z = contact_A_w[2] + verti
print "CASE 1"
elif axis[0] < 0:
if psi <= psi_active_transition:
pose_target.position.y = contact_A_w[1] - hori - active_distance*psi/psi_active_transition
else:
pose_target.position.y = contact_A_w[1] - hori - active_distance + active_distance_2*(psi-psi_active_transition)/(angle+1-psi_active_transition)
pose_target.position.z = contact_A_w[2] + verti
print "CASE 2"
elif axis[1] > 0:
pose_target.position.x = contact_A_w[0] - hori
pose_target.position.z = contact_A_w[2] + verti
print "CASE 3"
elif axis[1] < 0:
pose_target.position.x = contact_A_w[0] + hori
pose_target.position.z = contact_A_w[2] + verti
print "CASE 4"
pose_target.orientation.x = ori_waypoints[psi-1][0]
pose_target.orientation.y = ori_waypoints[psi-1][1]
pose_target.orientation.z = ori_waypoints[psi-1][2]
pose_target.orientation.w = ori_waypoints[psi-1][3]
waypoints.append(copy.deepcopy(pose_target))
(plan, fraction) = group.compute_cartesian_path(waypoints, 0.01, 0)
retimed_plan = group.retime_trajectory(robot.get_current_state(), plan, velocity)
group.execute(retimed_plan, wait=False)
opening_at_zero = config['max_opening']-2*config['finger_thickness']
psi = 0
while psi < angle:
pose = group.get_current_pose().pose
q_current = [pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w]
psi = 2*math.degrees(math.acos(np.dot(q_current, ori_initial)))
if psi > 100:
psi = -(psi-360)
a = config['delta_0'] * math.cos(math.radians(psi))
b = config['delta_0'] * math.sin(math.radians(psi))
c = config['object_thickness'] * math.cos(math.radians(psi))
d = config['object_thickness'] * math.sin(math.radians(psi))
opposite = a - d
width = b + c
#pos = int((opening_at_zero - width)/config['opening_per_count'])
Robotiq.goto(robotiq_client, pos=width+0.000, speed=config['gripper_speed'], force=config['gripper_force'], block=False) #0.006 for coin; 0.000 for book; 0.005 for poker
#if psi < 1.0 or psi > 47.0:
#print "psi= ", psi, " width= ", width
psi = round(psi, 2)
rospy.sleep(0.5) # TESTING TO SEE IF THE GRIPPER ACTION DOESNT LAG
return width
if __name__ == '__main__':
try:
rospy.init_node('regrasp', anonymous=True)
group.set_max_velocity_scaling_factor(1.0)
motion_primitives.set_joint([0, -90, 90, 90, 90, 0])
p = group.get_current_pose().pose
tilt.tilt([p.position.x,p.position.y,p.position.z-0.275], [0,-1,0], 60, 0.5)
regrasp([0,1,0], 90, 0.1)
except rospy.ROSInterruptException: pass
| 49.428256
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0
| 8
|
0111d12b2b6d6df86bf3e481cc12cb330c7921e6
| 4,914
|
py
|
Python
|
tests/test_entries.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | null | null | null |
tests/test_entries.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | 6
|
2021-09-06T08:23:09.000Z
|
2021-11-10T16:19:20.000Z
|
tests/test_entries.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | null | null | null |
from datetime import datetime
import pytest
import respx
from neuroio.constants import API_BASE_URL
from tests.utils import mock_query_params_all_combos
@respx.mock
def test_list_without_params200(client):
requests = mock_query_params_all_combos(
f"{API_BASE_URL}/v1/entries",
"limit=20",
"offset=0",
json={"results": [{"id": 1, "pid": "pid"}]},
)
response = client.entries.list()
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
def test_list_with_params200(client):
date_str = "2018-06-29"
date_obj = datetime.strptime(date_str, "%Y-%m-%d")
requests = mock_query_params_all_combos(
f"{API_BASE_URL}/v1/entries",
"sources_ids=1,2,3".replace(",", "%2C"),
f"date_from={date_str}",
"limit=20",
"offset=0",
json={"results": [{"id": 1, "pid": "pid"}]},
)
response = client.entries.list(
sources_ids=[1, 2, 3], date_from=date_obj.date()
)
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
@pytest.mark.asyncio
async def test_async_list_without_params_200(async_client):
requests = mock_query_params_all_combos(
f"{API_BASE_URL}/v1/entries",
"limit=20",
"offset=0",
json={"results": [{"id": 1, "pid": "pid"}]},
)
response = await async_client.entries.list()
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
@pytest.mark.asyncio
async def test_async_list_with_params_200(async_client):
date_str = "2018-06-29"
date_obj = datetime.strptime(date_str, "%Y-%m-%d")
requests = mock_query_params_all_combos(
f"{API_BASE_URL}/v1/entries",
"sources_ids=1,2,3".replace(",", "%2C"),
f"date_from={date_str}",
"limit=20",
"offset=0",
json={"results": [{"id": 1, "pid": "pid"}]},
)
response = await async_client.entries.list(
sources_ids=[1, 2, 3], date_from=date_obj.date()
)
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
def test_retrieve_200(client):
request = respx.get(f"{API_BASE_URL}/v1/entries/stats/pid/pid/").respond(
status_code=200,
json={"id": 1, "pid": "pid", "age": 27},
)
response = client.entries.get(pid="pid")
assert request.called
assert response.status_code == 200
assert response.json()["id"] == 1
assert response.json()["age"] == 27
@respx.mock
def test_retrieve_404(client):
request = respx.get(f"{API_BASE_URL}/v1/entries/stats/pid/pid/").respond(
status_code=404
)
response = client.entries.get(pid="pid")
assert request.called
assert response.status_code == 404
@respx.mock
@pytest.mark.asyncio
async def test_async_retrieve_200(async_client):
request = respx.get(f"{API_BASE_URL}/v1/entries/stats/pid/pid/").respond(
status_code=200,
json={"id": 1, "pid": "pid", "age": 27},
)
response = await async_client.entries.get(pid="pid")
assert request.called
assert response.status_code == 200
assert response.json()["id"] == 1
assert response.json()["age"] == 27
@respx.mock
@pytest.mark.asyncio
async def test_async_retrieve_404(async_client):
request = respx.get(f"{API_BASE_URL}/v1/entries/stats/pid/pid/").respond(
status_code=404
)
response = await async_client.entries.get(pid="pid")
assert request.called
assert response.status_code == 404
@respx.mock
def test_delete_204(client):
request = respx.delete(f"{API_BASE_URL}/v1/entries/1/").respond(
status_code=204
)
response = client.entries.delete(id=1)
assert request.called
assert response.status_code == 204
@respx.mock
def test_delete_400(client):
request = respx.delete(f"{API_BASE_URL}/v1/entries/1/").respond(
status_code=400
)
response = client.entries.delete(id=1)
assert request.called
assert response.status_code == 400
@respx.mock
@pytest.mark.asyncio
async def test_async_delete_204(async_client):
request = respx.delete(f"{API_BASE_URL}/v1/entries/1/").respond(
status_code=204
)
response = await async_client.entries.delete(id=1)
assert request.called
assert response.status_code == 204
@respx.mock
@pytest.mark.asyncio
async def test_async_delete_400(async_client):
request = respx.delete(f"{API_BASE_URL}/v1/entries/1/").respond(
status_code=400
)
response = await async_client.entries.delete(id=1)
assert request.called
assert response.status_code == 400
| 28.08
| 77
| 0.658323
| 684
| 4,914
| 4.53655
| 0.112573
| 0.090235
| 0.041895
| 0.042539
| 0.936513
| 0.905253
| 0.905253
| 0.905253
| 0.905253
| 0.905253
| 0
| 0.045809
| 0.191494
| 4,914
| 174
| 78
| 28.241379
| 0.735213
| 0
| 0
| 0.751773
| 0
| 0
| 0.141229
| 0.075702
| 0
| 0
| 0
| 0
| 0.22695
| 1
| 0.042553
| false
| 0
| 0.035461
| 0
| 0.078014
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
0158ec7cbe610f740bb95d6f803d32cecaed7121
| 5,531
|
py
|
Python
|
tests/test_scheduler.py
|
GCES-Pydemic/pydemic-ui
|
8e3d8bc9f73887edf6bd8ab78a4ead29fe8239ed
|
[
"MIT"
] | 1
|
2020-09-30T13:02:53.000Z
|
2020-09-30T13:02:53.000Z
|
tests/test_scheduler.py
|
GCES-Pydemic/pydemic-ui
|
8e3d8bc9f73887edf6bd8ab78a4ead29fe8239ed
|
[
"MIT"
] | 29
|
2020-10-03T02:10:38.000Z
|
2020-12-07T22:19:13.000Z
|
tests/test_scheduler.py
|
GCES-Pydemic/pydemic-ui
|
8e3d8bc9f73887edf6bd8ab78a4ead29fe8239ed
|
[
"MIT"
] | null | null | null |
from pydemic_ui.scheduler import *
class TestScheduler:
def test_if_a_task_is_scheduled(self):
def say_hello():
print("hello")
agendador = Scheduler()
agendador._clock = datetime_to_time
agendador._clock_args = datetime.datetime(2020, 9, 15, 12, 30)
agendador._sleep = lambda: sleep(0)
agendador.start()
data = datetime.datetime(2020, 9, 15, 12, 40, 3)
agendador.schedule(say_hello, data)
agendador.pause()
assert unix_time_to_string(agendador.tasks[0][0]) == "2020-09-15 12:40:03"
def test_if_scheduler_stops(self):
def say_hello():
print("hello")
agendador = Scheduler()
agendador._clock = datetime_to_time
agendador._clock_args = datetime.datetime(2020, 9, 15, 12, 30)
agendador._sleep = lambda: sleep(0)
agendador.start()
data = datetime.datetime(2020, 9, 15, 12, 40, 3)
agendador.schedule(say_hello, data)
agendador.stop()
assert len(agendador.tasks) == 0
def test_if_scheduler_pauses(self):
def say_hello():
print("hello")
agendador = Scheduler()
agendador._clock = datetime_to_time
agendador._clock_args = datetime.datetime(2020, 9, 15, 12, 30)
agendador._sleep = lambda: sleep(0)
agendador.start()
data = datetime.datetime(2020, 9, 15, 12, 40, 3)
agendador.schedule(say_hello, data)
agendador.pause()
assert len(agendador.tasks) == 1
def test_daily_schedule(self):
def say_hello():
print("hello")
scheduled_dates = []
expected_dates = []
agendador = Scheduler()
agendador._clock = datetime_to_time
agendador._clock_args = datetime.datetime(2020, 10, 1, 13, 30)
agendador._sleep = lambda: sleep(0)
agendador.start()
data = datetime.datetime(2020, 10, 1, 13, 30, 3)
expected_dates.append("2020-10-01 13:30:03")
agendador.schedule_daily(say_hello, data)
scheduled_dates.append(unix_time_to_string(agendador.tasks[0][0]))
day_counter = 1
while day_counter < 5:
if (
unix_time_to_string(agendador.tasks[0][0])
!= f"2020-10-0{day_counter} 13:30:03"
):
day_counter += 1
agendador._clock_args = datetime.datetime(2020, 10, day_counter, 13, 30)
agendador._clock_tick = 0
expected_dates.append(f"2020-10-0{day_counter} 13:30:03")
scheduled_dates.append(unix_time_to_string(agendador.tasks[0][0]))
agendador.stop()
assert scheduled_dates == expected_dates
def test_weekly_schedule(self):
def say_hello():
print("hello")
scheduled_dates = []
expected_dates = []
agendador = Scheduler()
agendador._clock = datetime_to_time
agendador._clock_args = datetime.datetime(2020, 10, 1, 13, 30)
agendador._sleep = lambda: sleep(0)
agendador.start()
data = datetime.datetime(2020, 10, 1, 13, 30, 3)
expected_dates.append("2020-10-01 13:30:03")
agendador.schedule_weekly(say_hello, data)
scheduled_dates.append(unix_time_to_string(agendador.tasks[0][0]))
day_counter = 1
while day_counter < 15:
if (
unix_time_to_string(agendador.tasks[0][0])
!= f"2020-10-0{day_counter} 13:30:03"
):
day_counter += 7
agendador._clock_args = datetime.datetime(2020, 10, day_counter, 13, 30)
agendador._clock_tick = 0
if day_counter >= 10:
expected_dates.append(f"2020-10-{day_counter} 13:30:03")
else:
expected_dates.append(f"2020-10-0{day_counter} 13:30:03")
scheduled_dates.append(unix_time_to_string(agendador.tasks[0][0]))
agendador.stop()
assert scheduled_dates == expected_dates
def test_monthly_schedule(self):
def say_hello():
return "hello"
scheduled_dates = []
expected_dates = []
agendador = Scheduler()
agendador._clock = datetime_to_time
agendador._clock_args = datetime.datetime(2020, 3, 15, 13, 30)
agendador._sleep = lambda: sleep(0)
agendador.start()
data = datetime.datetime(2020, 3, 15, 13, 30, 3)
expected_dates.append("2020-03-15 13:30:03")
agendador.schedule_monthly(say_hello, data)
scheduled_dates.append(unix_time_to_string(agendador.tasks[0][0]))
month_counter = 3
while month_counter < 10:
if (
unix_time_to_string(agendador.tasks[0][0])
!= f"2020-{month_counter}-15 13:30:03"
):
month_counter += 1
agendador._clock_args = datetime.datetime(
2020, month_counter, 15, 13, 30, 30
)
agendador._clock_tick = 0
if month_counter > 8:
expected_dates.append(f"2020-{month_counter+1}-15 13:30:03")
else:
expected_dates.append(f"2020-0{month_counter+1}-15 13:30:03")
sleep(0.1)
scheduled_dates.append(unix_time_to_string(agendador.tasks[0][0]))
agendador.stop()
assert scheduled_dates == expected_dates
| 30.558011
| 88
| 0.581269
| 662
| 5,531
| 4.616314
| 0.10423
| 0.026178
| 0.098168
| 0.052356
| 0.899542
| 0.876636
| 0.844895
| 0.82199
| 0.796139
| 0.781741
| 0
| 0.098532
| 0.310071
| 5,531
| 180
| 89
| 30.727778
| 0.702306
| 0
| 0
| 0.726563
| 0
| 0
| 0.065268
| 0.033086
| 0
| 0
| 0
| 0
| 0.046875
| 1
| 0.09375
| false
| 0
| 0.007813
| 0.007813
| 0.117188
| 0.039063
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6d790fab551c9cc06738dca20e52c05b7875a5bc
| 128
|
py
|
Python
|
python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_1/_pkg0_1_0_1_1/_mod0_1_0_1_1_3.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_1/_pkg0_1_0_1_1/_mod0_1_0_1_1_3.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_1/_pkg0_1_0_1_1/_mod0_1_0_1_1_3.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
name0_1_0_1_1_3_0 = None
name0_1_0_1_1_3_1 = None
name0_1_0_1_1_3_2 = None
name0_1_0_1_1_3_3 = None
name0_1_0_1_1_3_4 = None
| 14.222222
| 24
| 0.820313
| 40
| 128
| 1.875
| 0.175
| 0.4
| 0.466667
| 0.533333
| 0.88
| 0.88
| 0.746667
| 0
| 0
| 0
| 0
| 0.318182
| 0.140625
| 128
| 9
| 25
| 14.222222
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
6dc39af806917e4635cacd85b56e3c4dd9c456b4
| 350
|
py
|
Python
|
bminf/functions/gelu.py
|
THUCSTHanxu13/BMInf
|
59e8903366ed53615d8af0a61e15b9f932042dcc
|
[
"Apache-2.0"
] | null | null | null |
bminf/functions/gelu.py
|
THUCSTHanxu13/BMInf
|
59e8903366ed53615d8af0a61e15b9f932042dcc
|
[
"Apache-2.0"
] | null | null | null |
bminf/functions/gelu.py
|
THUCSTHanxu13/BMInf
|
59e8903366ed53615d8af0a61e15b9f932042dcc
|
[
"Apache-2.0"
] | null | null | null |
from ..backend import create_ufunc
gelu_kernel = create_ufunc('bms_gelu', ('ff->f', 'ee->e'), 'out0 = 0.5 * in0 * (1.0 + tanh(0.7978845608028654 * in0 * (1.0 + 0.044715 * in0 * in0))) * in1')
gelu_raw_kernel = create_ufunc('bms_gelu_raw', ('f->f', 'e->e'), 'out0 = 0.5 * in0 * (1.0 + tanh(0.7978845608028654 * in0 * (1.0 + 0.044715 * in0 * in0)))')
| 70
| 156
| 0.605714
| 60
| 350
| 3.383333
| 0.366667
| 0.078818
| 0.098522
| 0.197044
| 0.738916
| 0.502463
| 0.502463
| 0.502463
| 0.502463
| 0.502463
| 0
| 0.240678
| 0.157143
| 350
| 4
| 157
| 87.5
| 0.447458
| 0
| 0
| 0
| 0
| 0.666667
| 0.628571
| 0.131429
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
6ddf7a9054812193e94294f749730b1d13cdbc36
| 12,878
|
py
|
Python
|
untils/DataProcessing.py
|
cyzLoveDream/text_dnn_classifier
|
9986dd8ac66b2ac4731e28209eba524be1283d47
|
[
"Apache-2.0"
] | 17
|
2017-12-14T07:34:59.000Z
|
2021-03-23T12:29:40.000Z
|
untils/DataProcessing.py
|
cyzLoveDream/text_dnn_classifier
|
9986dd8ac66b2ac4731e28209eba524be1283d47
|
[
"Apache-2.0"
] | null | null | null |
untils/DataProcessing.py
|
cyzLoveDream/text_dnn_classifier
|
9986dd8ac66b2ac4731e28209eba524be1283d47
|
[
"Apache-2.0"
] | 2
|
2017-12-14T07:57:56.000Z
|
2018-06-08T09:42:01.000Z
|
import timeit
import tensorflow as tf
import pandas as pd
from tqdm import tqdm
class DataProcessing():
def __init__(self, in_path, out_path):
if in_path == False:
self.out_path = out_path
elif out_path == False:
self.in_path = in_path
elif in_path == False and out_path == False:
pass
else:
self.in_path = in_path
self.out_path = out_path
def train_txt2tfrecords(self):
"""
将train转化为tfrecords格式的文件
:param in_path:
:param out_path:
:return:
"""
print("\nStart to convert {} to {}\n".format(self.in_path,self.out_path))
start_time = timeit.default_timer()
writer = tf.python_io.TFRecordWriter(self.out_path)
num = 0
with open(self.in_path,mode="r",encoding="utf-8") as rf:
lines = rf.readlines()
for line in tqdm(lines):
num += 1
data = line.split("\t")
try:
txt_id = [bytes(data[0],"utf-8")]
txt_title = [bytes(data[1],"utf-8")]
txt_content = [bytes(data[2],"utf-8")]
txt_label = [bytes(data[3][0:-1],"utf-8")]
except:
txt_id = [bytes(str(data[0])),"utf-8"]
txt_title = [bytes(str(" ").strip()),"utf-8"]
txt_content = [bytes(str(" ").strip(),"utf-8")]
txt_label = [bytes(str(data[3][0:-1]),"utf-8")]
example = tf.train.Example(features=tf.train.Features(feature={
"txt_id":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_id)),
"txt_title":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_title)),
"txt_content":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_content)),
"txt_label":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_label))
}))
writer.write(example.SerializeToString()) # 序列化为字符串
writer.close()
print("Successfully convert {} to {}".format(self.in_path,self.out_path))
end_time = timeit.default_timer()
print("\nThe pretraining process ran for {0} minutes\n".format((end_time - start_time) / 60))
print("the count is: ",num)
return num, self.out_path
def test_txt2tfrecords(self):
"""
将测试集转化为tfrecords文件格式
:param in_path:
:param out_path:
:return:
"""
print("\nStart to convert {} to {}\n".format(self.in_path,self.out_path))
start_time = timeit.default_timer()
writer = tf.python_io.TFRecordWriter(self.out_path)
num = 0
with open(self.in_path,mode="r",encoding="gbk") as rf:
lines = rf.readlines()
for line in tqdm(lines):
num += 1
data = line.strip().split("\t")
try:
txt_id = [bytes(data[0],"utf-8")]
txt_title = [bytes(data[1],"utf-8")]
txt_content = [bytes(data[2],"utf-8")]
except:
txt_id = [bytes(str(data[0]),"utf-8")]
txt_title = [bytes(str(" "),"utf-8")]
txt_content = [bytes(str(" "),"utf-8")]
# 将数据转化为原生 bytes
example = tf.train.Example(features=tf.train.Features(feature={
"txt_id":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_id)),
"txt_title":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_title)),
"txt_content":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_content))
}))
writer.write(example.SerializeToString()) # 序列化为字符串
writer.close()
print("Successfully convert {} to {}".format(self.in_path,self.out_path))
end_time = timeit.default_timer()
print("\nThe pretraining process ran for {0} minutes\n".format((end_time - start_time) / 60))
print("the count is: ",num)
return num,self.out_path
def parase_tfrecords_to_dataFrame(self, data_shape):
"""
解析预测完的tfrecords,并且生成需要提交的文件
:param filename:
:param data_shape:
:return:
"""
data_list = []
with tf.Session() as sess:
filename_queue = tf.train.string_input_producer([self.in_path],shuffle=False)
read = tf.TFRecordReader()
_,serialized_example = read.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
"txt_id": tf.FixedLenFeature([],tf.string),
"label": tf.FixedLenFeature([],tf.float32),
})
txt_id = features['txt_id']
label = features["label"]
init_op = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in tqdm(range(data_shape)):
content_list = sess.run([txt_id,label])
c_l = []
c_l.append(str(content_list[0],"utf-8"))
c_l.append(content_list[1])
data_list.append(c_l)
coord.request_stop()
coord.join(threads)
sess.close()
data_pd = pd.DataFrame(data_list,columns=["txt_id","label"])
data_pd["label"] = data_pd["label"].apply(
lambda x: "POSITIVE" if x > 0.50 else "NEGATIVE")
data_pd.to_csv(self.out_path,header=False,index=False)
def load_raw_train_data(self, data_shape):
"""
载入训练数据
:param filename:
:return:
"""
print("\nbegin load train data\n")
data_list = []
with tf.Session() as sess:
filename_queue = tf.train.string_input_producer([self.in_path],shuffle=False,seed=0)
read = tf.TFRecordReader()
_,serialized_example = read.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
"txt_id": tf.FixedLenFeature([],tf.string),
"txt_title": tf.FixedLenFeature([],tf.string),
"txt_content": tf.FixedLenFeature([],tf.string),
"txt_label": tf.FixedLenFeature([],tf.string)
})
txt_id = features["txt_id"]
txt_title = features["txt_title"]
txt_content = features["txt_content"]
txt_label = features["txt_label"]
init_op = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in tqdm(range(data_shape)):
content_list = sess.run([txt_id,txt_title,txt_content,txt_label])
c_l = []
for d in content_list:
c_l.append(str(d,"utf-8"))
data_list.append(c_l)
coord.request_stop()
coord.join(threads)
sess.close()
data_pd = pd.DataFrame(data_list,columns=["txt_id","txt_title","txt_content","txt_label"])
return data_pd
def load_raw_test_data(self, data_shape):
"""
验证转存之后的数据是否相对应
:param input_filename:
:param data_shape: 数据总共有多少条
:return:
"""
print("\nbegin load test data\n")
data_list = []
with tf.Session() as sess:
filename_queue = tf.train.string_input_producer([self.in_path],shuffle=False,seed=0)
read = tf.TFRecordReader()
_,serialized_example = read.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
"txt_id": tf.FixedLenFeature([],tf.string),
"txt_title": tf.FixedLenFeature([],tf.string),
"txt_content": tf.FixedLenFeature([],tf.string),
# "txt_label": tf.FixedLenFeature([],tf.string)
})
txt_id = features["txt_id"]
txt_title = features["txt_title"]
txt_content = features["txt_content"]
# txt_label = features["txt_label"]
init_op = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in tqdm(range(data_shape)):
content_list = sess.run([txt_id,txt_title,txt_content])
c_l = []
for d in content_list:
c_l.append(str(d,"utf-8"))
data_list.append(c_l)
coord.request_stop()
coord.join(threads)
sess.close()
data_pd = pd.DataFrame(data_list,columns=["txt_id","txt_title","txt_content"])
return data_pd
def train_feature_txt2tfrecords(self, data):
"""
转化训练特征数据为tfrecords格式
:param data:
:return:
"""
print("\nStart to convert to {}\n".format(self.out_path))
start_time = timeit.default_timer()
writer = tf.python_io.TFRecordWriter(self.out_path)
for line in tqdm(data):
label = [int(line[0])]
feature = [bytes(line[1],"utf-8")]
# print(name)
# 将数据转化为原生 bytes
example = tf.train.Example(features=tf.train.Features(feature={
"label":
tf.train.Feature(int64_list=tf.train.Int64List(value=label)),
"feature":
tf.train.Feature(bytes_list=tf.train.BytesList(value=feature))
}))
writer.write(example.SerializeToString()) # 序列化为字符串
writer.close()
print("Successfully convert to {}".format(self.out_path))
end_time = timeit.default_timer()
print("\nThe pretraining process ran for {0} minutes\n".format((end_time - start_time) / 60))
def test_featuure_txt2tfrecords(self,data):
"""
转化测试特征数据为tfrecords格式
:param data:
:return:
"""
print("\nStart to convert to {}\n".format(self.out_path))
start_time = timeit.default_timer()
writer = tf.python_io.TFRecordWriter(self.out_path)
for line in tqdm(data):
txt_id = [bytes(line[0],"utf-8")]
feature = [bytes(line[1],"utf-8")]
# print(name)
# 将数据转化为原生 bytes
example = tf.train.Example(features=tf.train.Features(feature={
"txt_id":
tf.train.Feature(bytes_list=tf.train.BytesList(value=txt_id)),
"feature":
tf.train.Feature(bytes_list=tf.train.BytesList(value=feature))
}))
writer.write(example.SerializeToString()) # 序列化为字符串
writer.close()
print("Successfully convert to {}".format(self.out_path))
end_time = timeit.default_timer()
print("\nThe pretraining process ran for {0} minutes\n".format((end_time - start_time) / 60))
def load_tfrecords_train_feature_data_train(self,data_shape):
"""
载入训练数据
:param filename:
:return:
"""
data_list = []
with tf.Session() as sess:
filename_queue = tf.train.string_input_producer([self.in_path],shuffle=False,seed=0)
read = tf.TFRecordReader()
_,serialized_example = read.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
"label": tf.FixedLenFeature([],tf.int64),
"feature": tf.FixedLenFeature([],tf.string)
})
label = features["label"]
feature = features["feature"]
init_op = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in tqdm(range(data_shape)):
content_list = sess.run([label,feature])
c_l = []
c_l.append([content_list[0],eval(str(content_list[1],"utf-8"))])
data_list.extend(c_l)
coord.request_stop()
coord.join(threads)
sess.close()
print("have been data's count is: ",data_shape)
return data_list
def load_tfrecords_test_feature_data_train(self,data_shape):
"""
载入训练数据
:param filename:
:return:
"""
data_list = []
with tf.Session() as sess:
filename_queue = tf.train.string_input_producer([self.in_path],shuffle=False,seed=0)
read = tf.TFRecordReader()
_,serialized_example = read.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
"txt_id": tf.FixedLenFeature([],tf.string),
"feature": tf.FixedLenFeature([],tf.string)
})
feature = features["feature"]
txt_id = features["txt_id"]
init_op = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in tqdm(range(data_shape)):
feature_data = sess.run([txt_id,feature])
f_l = str(feature_data[1],"utf-8")
data_list.append([str(feature_data[0],"utf-8"),eval(f_l)])
coord.request_stop()
coord.join(threads)
sess.close()
print("have been data's count is: ",data_shape)
return data_list
def main():
"""
测试当前类的方法的主方法入口
:return:
"""
# DataProcessing("../data_example/test.tsv","../data_example/test.tfrecords").test_txt2tfrecords()
# DataProcessing("../data_example/train.tsv","../data_example/train.tfrecords").train_txt2tfrecords()
# DataProcessing("../feature_data/tsz_submission_12.tfrecords","../submission/sub.csv").parase_tfrecords_to_dataFrame(1500)
# data = DataProcessing("../data_example/train.tfrecords",False).load_train_data(2000)
# data = DataProcessing("../data_example/test.tfrecords",False).load_test_data(1500)
# print(data.head())
if __name__ == '__main__':
main()
| 36.481586
| 124
| 0.643112
| 1,685
| 12,878
| 4.693769
| 0.105638
| 0.039828
| 0.023644
| 0.037931
| 0.802251
| 0.764446
| 0.754457
| 0.749652
| 0.747629
| 0.747629
| 0
| 0.009727
| 0.20966
| 12,878
| 353
| 125
| 36.481586
| 0.767341
| 0.091241
| 0
| 0.749077
| 0
| 0
| 0.091729
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.04059
| false
| 0.00369
| 0.01476
| 0
| 0.081181
| 0.066421
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 1
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| null | 0
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|
0
| 7
|
6de63e6f568991d5327951ebe98c34a8e7f0a16f
| 8,056
|
py
|
Python
|
Test_Scripts/undo_move.py
|
karlflores/WatchYourBackProject
|
00a7c32e46ea0b75580d17ea6a22372e4a005627
|
[
"Unlicense"
] | null | null | null |
Test_Scripts/undo_move.py
|
karlflores/WatchYourBackProject
|
00a7c32e46ea0b75580d17ea6a22372e4a005627
|
[
"Unlicense"
] | null | null | null |
Test_Scripts/undo_move.py
|
karlflores/WatchYourBackProject
|
00a7c32e46ea0b75580d17ea6a22372e4a005627
|
[
"Unlicense"
] | null | null | null |
from DepreciatedBoard.Board import Board
from Constants import constant
from Agents.Minimax import Minimax
# create a new board game
board_game = Board()
board_game.print_board()
print()
print()
# create the starting node -- this node is black
node = Minimax.create_node(board_game, constant.BLACK_PIECE, None)
node.board.print_board()
print(node.available_moves)
print(node.colour)
print()
# to create a child node we apply one of the move from black to the next node
child = Minimax.create_node(node.board,node.colour,(3,2))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(3,3))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(6,2))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(7,2))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(3,5))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(5,2))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(3,2))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(4,3))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(6,3))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(1,3))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(2,2))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(7,6))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(0,1))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(0,6))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(5,5))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(2,5))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(5,3))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(5,4))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(2,3))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(3,3))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(4,4))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(5,6))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(7,3))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),(4,5))
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
print("UNDO MOVE")
child.board.undo_move()
child.board.print_board()
print(child.available_moves)
print("MOVE COUNTER ",end='')
print(child.board.move_counter)
print(child.board.piece_pos)
print()
print("eliminated pieces")
print(child.board.eliminated_pieces)
print()
'''
print("UNDO MOVE")
child.board.undo_move()
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
'''
| 28.567376
| 85
| 0.796425
| 1,221
| 8,056
| 5.063882
| 0.040131
| 0.208637
| 0.189229
| 0.155588
| 0.935145
| 0.935145
| 0.935145
| 0.935145
| 0.935145
| 0.935145
| 0
| 0.0063
| 0.054245
| 8,056
| 281
| 86
| 28.669039
| 0.805224
| 0.018123
| 0
| 0.863636
| 0
| 0
| 0.079081
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.012397
| 0
| 0.012397
| 0.876033
| 0
| 0
| 0
| null | 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 10
|
6dfdab7e58d66249755e432d7cd8edc5027f6ff0
| 832
|
py
|
Python
|
generate_test_case.py
|
shadlyd15/TinyTest
|
d410ae76ca992f9bc468c1b5b2da5226cbf12bde
|
[
"MIT"
] | 6
|
2019-01-17T06:47:02.000Z
|
2020-12-11T13:51:56.000Z
|
generate_test_case.py
|
shadlyd15/tinytest
|
c29da09366bace64dc83f8ffab201a71617c7528
|
[
"MIT"
] | 1
|
2019-05-15T15:34:36.000Z
|
2019-05-15T15:34:36.000Z
|
generate_test_case.py
|
shadlyd15/tinytest
|
c29da09366bace64dc83f8ffab201a71617c7528
|
[
"MIT"
] | 2
|
2019-01-17T06:47:03.000Z
|
2019-05-09T06:01:10.000Z
|
for number in range(50):
print("ADD_TINY_TEST(test_" + str(number) + "){")
print(" int i = rand()%50;")
print(" ASSERT_TEST_RESULT(i != " + str(number + 1) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 2) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 3) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 4) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 5) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 6) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 7) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 8) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 9) + ");")
# print(" ASSERT_TEST_RESULT(i != " + str(number + 10) + ");")
print("}\r\n")
# for number in range(50):
# print(" RUN_TINY_TEST(test_" + str(number) + ");")
| 48.941176
| 63
| 0.55649
| 112
| 832
| 3.901786
| 0.25
| 0.24714
| 0.343249
| 0.480549
| 0.910755
| 0.814645
| 0.709382
| 0
| 0
| 0
| 0
| 0.024927
| 0.180288
| 832
| 17
| 64
| 48.941176
| 0.615836
| 0.741587
| 0
| 0
| 0
| 0
| 0.352941
| 0
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.8
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
a300a98541cbea61b47f345138f8cefbae13334b
| 1,279
|
py
|
Python
|
pre_exam/honey_harvest.py
|
PetkoAndreev/Python-basics
|
a376362548380ae50c7c707551cb821547f44402
|
[
"MIT"
] | null | null | null |
pre_exam/honey_harvest.py
|
PetkoAndreev/Python-basics
|
a376362548380ae50c7c707551cb821547f44402
|
[
"MIT"
] | null | null | null |
pre_exam/honey_harvest.py
|
PetkoAndreev/Python-basics
|
a376362548380ae50c7c707551cb821547f44402
|
[
"MIT"
] | null | null | null |
type_flower = input()
flower_number = int(input())
season = input()
if type_flower == 'Sunflower':
if season == 'Spring':
total_honey = flower_number * 10
elif season == 'Summer':
total_honey = (flower_number * 8) + 0.1 * (flower_number * 8)
else:
total_honey = (flower_number * 12) - 0.05 * (flower_number * 12)
elif type_flower == 'Daisy':
if season == 'Spring':
total_honey = (flower_number * 12) + 0.1 * (flower_number * 12)
elif season == 'Summer':
total_honey = (flower_number * 8) + 0.1 * (flower_number * 8)
else:
total_honey = (flower_number * 6) - 0.05 * (flower_number * 6)
elif type_flower == 'Lavender':
if season == 'Spring':
total_honey = flower_number * 12
elif season == 'Summer':
total_honey = (flower_number * 8) + 0.1 * (flower_number * 8)
else:
total_honey = (flower_number * 6) - 0.05 * (flower_number * 6)
else:
if season == 'Spring':
total_honey = (flower_number * 10) + 0.1 * (flower_number * 10)
elif season == 'Summer':
total_honey = (flower_number * 12) + 0.1 * (flower_number * 12)
else:
total_honey = (flower_number * 6) - 0.05 * (flower_number * 6)
print(f'Total honey harvested: {total_honey:.2f}')
| 34.567568
| 72
| 0.598124
| 168
| 1,279
| 4.315476
| 0.160714
| 0.38069
| 0.264828
| 0.364138
| 0.769655
| 0.769655
| 0.765517
| 0.765517
| 0.627586
| 0.627586
| 0
| 0.060063
| 0.258014
| 1,279
| 37
| 73
| 34.567568
| 0.703899
| 0
| 0
| 0.65625
| 0
| 0
| 0.085938
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.03125
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
097f982477954c04d75fc73b6d86264b48bb81b1
| 7,347
|
py
|
Python
|
tests/test_calculator.py
|
cardwellshayne/calculator
|
09d92c5ad2fa49384f81e0915d7e1f1755f88c15
|
[
"MIT"
] | 2
|
2019-01-23T06:20:52.000Z
|
2019-05-01T08:08:31.000Z
|
tests/test_calculator.py
|
cardwellshayne/calculator
|
09d92c5ad2fa49384f81e0915d7e1f1755f88c15
|
[
"MIT"
] | null | null | null |
tests/test_calculator.py
|
cardwellshayne/calculator
|
09d92c5ad2fa49384f81e0915d7e1f1755f88c15
|
[
"MIT"
] | null | null | null |
#! /usr/bin/python3
"""
Title: test_calculator.py
Author: Shayne Cardwell
Description: A Testing module to test the Calculator class
"""
import unittest
from app import calculator
class MyCalculatorTest(unittest.TestCase):
def test_add_non_int_non_float_exception(self):
"""Validates that an exception raises when adding strings"""
data_sets = [[], '', {}]
for data_set in data_sets:
with self.assertRaises(TypeError):
calculator.add(data_set, data_set)
def test_add_integer_addition(self):
"""Validates that add yields the expected integer results"""
self.assertEqual(calculator.add(2, 3), 5)
def test_add_float_addtion(self):
"""Validates that add yields the expected flotation results"""
self.assertEqual(calculator.add(2.0, 3.0), 5.0)
def test_add_float_and_integer_addtion(self):
"""Validates that add yields the expected flotation results"""
self.assertEqual(calculator.add(2.0, 3), 5.0)
def test_subtract_non_int_non_float_exception(self):
"""
Validates that an exception raises when subtracting a non float
or int
"""
data_sets = [[], '', {}]
for data_set in data_sets:
with self.assertRaises(TypeError):
calculator.subtract(data_set, data_set)
def test_subtract_integer_subtraction(self):
"""Validates that subtract yields the expected integer results"""
self.assertEqual(calculator.subtract(10, 6), 4)
def test_subtract_float_subtraction(self):
"""Validates that subtract yields the expected flotation results"""
self.assertEqual(calculator.subtract(10.0, 6.0), 4.0)
def test_subtract_float_and_integer_subtraction(self):
"""Validates that subtract yields the expected flotation results"""
self.assertEqual(calculator.subtract(10.0, 6), 4.0)
def test_multiply_non_int_non_float_exception(self):
"""Validates that an exception raises when adding strings"""
data_sets = [[], '', {}]
for data_set in data_sets:
with self.assertRaises(TypeError):
calculator.multiply(data_set, data_set)
def test_multiply_integer_multiplication(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.multiply(6, 3), 18)
def test_multiply_float_multiplication(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.multiply(6.0, 3), 18.0)
def test_multiply_integer_zero_mulitplicand(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.multiply(0, 3), 0)
def test_multiply_integer_zero_muliplier(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.multiply(6, 0), 0)
def test_multiply_float_zero_mulitplicand(self):
"""Validates that add yields the expected results"""
print(calculator.multiply(0.0, 3))
self.assertEqual(calculator.multiply(0.0, 3), 0.0)
def test_divide_non_int_non_float_exception(self):
"""Validates that an exception raises when adding strings"""
data_sets = [[], '', {}]
for data_set in data_sets:
with self.assertRaises(TypeError):
calculator.divide(data_set, data_set)
def test_divide_integer_by_zero_integer(self):
"""Validates that an exception raises when dividing by 0"""
with self.assertRaises(ZeroDivisionError):
calculator.divide(4, 0)
def test_divide_integer_by_zero_float(self):
"""Validates that an exception raises when dividing by 0"""
with self.assertRaises(ZeroDivisionError):
calculator.divide(4, 0.0)
def test_divide_integer(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.divide(8, 2), 4)
def test_divide_remainder_integer(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.divide(5, 2), 2)
def test_divide_float(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.divide(8.0, 2.0), 4.0)
def test_divide_remainder_float(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.divide(5.0, 2.0), 2.0)
def test_modulus_non_int_non_float_exception(self):
"""Validates that an exception raises when adding strings"""
data_sets = [[], '', {}]
for data_set in data_sets:
with self.assertRaises(TypeError):
calculator.modulus(data_set, data_set)
def test_modulus_integer_by_zero(self):
"""Validates that an exception raises when dividing by 0"""
with self.assertRaises(ZeroDivisionError):
calculator.modulus(4, 0)
def test_modulus_integer(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.modulus(8, 2), 0)
def test_modulus_integer_remainder(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.modulus(5, 2), 1)
def test_modulus_float_by_zero(self):
"""Validates that an exception raises when dividing by 0"""
with self.assertRaises(ZeroDivisionError):
calculator.modulus(4, 0.0)
def test_modulus_float(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.modulus(8.0, 2.0), 0.0)
def test_modulus_float_remainder(self):
"""Validates that add yields the expected results"""
self.assertEqual(calculator.modulus(5.0, 2.0), 1.0)
def test_power_non_int_non_float_exception(self):
"""Validates that an exception raises when adding strings"""
data_sets = [[], '', {}]
for data_set in data_sets:
with self.assertRaises(TypeError):
calculator.power(data_set, data_set)
def test_power_integer_multiplication(self):
"""Validates that power yields the expected results"""
self.assertEqual(calculator.power(2, 3), 8)
def test_power_integer_multiplication_2(self):
"""Validates that power yields the expected results"""
self.assertEqual(calculator.power(3, 3), 27)
def test_power_integer_multiplication_3(self):
"""Validates that power yields the expected results"""
self.assertEqual(calculator.power(2, 2), 4)
def test_power_integer_multiplication_4(self):
"""Validates that power yields the expected results"""
self.assertEqual(calculator.power(3, 2), 9)
def test_power_integer_multiplication_5(self):
"""Validates that power yields the expected results"""
self.assertEqual(calculator.power(5, 7), 78125)
def test_power_integer_zero_exponent(self):
"""Validates that power yields the expected results"""
self.assertEqual(calculator.power(6, 0), 1)
def test_power_float_zero_base(self):
"""Validates that power yields the expected results"""
print(calculator.power(0, 3))
self.assertEqual(calculator.power(0, 3), 0)
if __name__ == '__main__':
unittest.main()
| 38.873016
| 75
| 0.673472
| 920
| 7,347
| 5.182609
| 0.097826
| 0.052852
| 0.128356
| 0.161074
| 0.855076
| 0.782089
| 0.71896
| 0.714765
| 0.685193
| 0.671141
| 0
| 0.024211
| 0.224173
| 7,347
| 188
| 76
| 39.079787
| 0.812281
| 0.272492
| 0
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| 0
| 0
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| 0
| 0.356436
| 1
| 0.356436
| false
| 0
| 0.019802
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| 0.386139
| 0.019802
| 0
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| null | 0
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
61fab5594c972fdc237b4b789cd4b04af85b5d6d
| 608
|
py
|
Python
|
FuzzingTool_Dialog_AskStartFuzzing.py
|
Ryu-Miyaki/Fuzz4B
|
8546f165d4dbdd97eb6ab5a6f4c445ee81ec364b
|
[
"MIT"
] | 16
|
2020-06-25T11:56:59.000Z
|
2022-02-05T14:00:12.000Z
|
FuzzingTool_Dialog_AskStartFuzzing.py
|
Ryu-Miyaki/Fuzz4B
|
8546f165d4dbdd97eb6ab5a6f4c445ee81ec364b
|
[
"MIT"
] | null | null | null |
FuzzingTool_Dialog_AskStartFuzzing.py
|
Ryu-Miyaki/Fuzz4B
|
8546f165d4dbdd97eb6ab5a6f4c445ee81ec364b
|
[
"MIT"
] | null | null | null |
"""Subclass of Dialog_AskStartFuzzing, which is generated by wxFormBuilder."""
import wx
import FuzzingTool
# Implementing Dialog_AskStartFuzzing
class FuzzingTool_Dialog_AskStartFuzzing( FuzzingTool.Dialog_AskStartFuzzing ):
def __init__( self, parent ):
FuzzingTool.Dialog_AskStartFuzzing.__init__( self, parent )
# Handlers for Dialog_AskStartFuzzing events.
def bSizer_OKorCancelOnCancelButtonClick( self, event ):
# TODO: Implement bSizer_OKorCancelOnCancelButtonClick
pass
def bSizer_OKorCancelOnOKButtonClick( self, event ):
# TODO: Implement bSizer_OKorCancelOnOKButtonClick
pass
| 28.952381
| 79
| 0.820724
| 58
| 608
| 8.275862
| 0.482759
| 0.2625
| 0.2
| 0.091667
| 0.116667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120066
| 608
| 20
| 80
| 30.4
| 0.897196
| 0.419408
| 0
| 0.222222
| 1
| 0
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| 0
| 0
| 0
| 0
| 0.05
| 0
| 1
| 0.333333
| false
| 0.222222
| 0.222222
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 1
| 1
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| 0
| 0
| 0
| 0
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| 0
| 0
| 0
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| 1
| 0
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| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 7
|
28c7c40ca9d10445f10745c0c825cf34d8dcc069
| 13,564
|
py
|
Python
|
python-idx-demo/Demo/LoginRadius/api/account/role_api.py
|
akshay-s-770/login-page-demos
|
d2006f3af0d893488586d0da5ad54d84f99e2f92
|
[
"MIT"
] | 1
|
2020-08-01T22:11:13.000Z
|
2020-08-01T22:11:13.000Z
|
python-idx-demo/Demo/LoginRadius/api/account/role_api.py
|
akshay-s-770/login-page-demos
|
d2006f3af0d893488586d0da5ad54d84f99e2f92
|
[
"MIT"
] | 4
|
2021-09-28T05:58:17.000Z
|
2022-03-31T01:51:36.000Z
|
python-idx-demo/Demo/LoginRadius/api/account/role_api.py
|
shvam0000/login-page-demos
|
0cbf3df58243326f77e57b2d7dbca868f830107b
|
[
"MIT"
] | 13
|
2020-06-23T20:45:32.000Z
|
2022-02-14T14:26:01.000Z
|
# -- coding: utf-8 --
# Created by LoginRadius Development Team
# Copyright 2019 LoginRadius Inc. All rights reserved.
#
class RoleApi:
def __init__(self, lr_object):
"""
:param lr_object: this is the reference to the parent LoginRadius object.
"""
self._lr_object = lr_object
def get_roles_by_uid(self, uid):
"""API is used to retrieve all the assigned roles of a particular User.
Args:
uid: UID, the unified identifier for each user account
Returns:
Response containing Definition of Complete Roles data
18.6
"""
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/role"
return self._lr_object.execute("GET", resource_path, query_parameters, None)
def assign_roles_by_uid(self, account_roles_model, uid):
"""This API is used to assign your desired roles to a given user.
Args:
account_roles_model: Model Class containing Definition of payload for Create Role API
uid: UID, the unified identifier for each user account
Returns:
Response containing Definition of Complete Roles data
18.7
"""
if(account_roles_model is None):
raise Exception(self._lr_object.get_validation_message("account_roles_model"))
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/role"
return self._lr_object.execute("PUT", resource_path, query_parameters, account_roles_model)
def unassign_roles_by_uid(self, account_roles_model, uid):
"""This API is used to unassign roles from a user.
Args:
account_roles_model: Model Class containing Definition of payload for Create Role API
uid: UID, the unified identifier for each user account
Returns:
Response containing Definition of Delete Request
18.8
"""
if(account_roles_model is None):
raise Exception(self._lr_object.get_validation_message("account_roles_model"))
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/role"
return self._lr_object.execute("DELETE", resource_path, query_parameters, account_roles_model)
def get_role_context_by_uid(self, uid):
"""This API Gets the contexts that have been configured and the associated roles and permissions.
Args:
uid: UID, the unified identifier for each user account
Returns:
Complete user RoleContext data
18.9
"""
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/rolecontext"
return self._lr_object.execute("GET", resource_path, query_parameters, None)
def get_role_context_by_context_name(self, context_name):
"""The API is used to retrieve role context by the context name.
Args:
context_name: Name of context
Returns:
Complete user RoleContext data
18.10
"""
if(self._lr_object.is_null_or_whitespace(context_name)):
raise Exception(self._lr_object.get_validation_message("context_name"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/rolecontext/" + context_name
return self._lr_object.execute("GET", resource_path, query_parameters, None)
def update_role_context_by_uid(self, account_role_context_model, uid):
"""This API creates a Context with a set of Roles
Args:
account_role_context_model: Model Class containing Definition of RoleContext payload
uid: UID, the unified identifier for each user account
Returns:
Complete user RoleContext data
18.11
"""
if(account_role_context_model is None):
raise Exception(self._lr_object.get_validation_message("account_role_context_model"))
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/rolecontext"
return self._lr_object.execute("PUT", resource_path, query_parameters, account_role_context_model)
def delete_role_context_by_uid(self, context_name, uid):
"""This API Deletes the specified Role Context
Args:
context_name: Name of context
uid: UID, the unified identifier for each user account
Returns:
Response containing Definition of Delete Request
18.12
"""
if(self._lr_object.is_null_or_whitespace(context_name)):
raise Exception(self._lr_object.get_validation_message("context_name"))
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/rolecontext/" + context_name
return self._lr_object.execute("DELETE", resource_path, query_parameters, None)
def delete_roles_from_role_context_by_uid(self, context_name, role_context_remove_role_model, uid):
"""This API Deletes the specified Role from a Context.
Args:
context_name: Name of context
role_context_remove_role_model: Model Class containing Definition of payload for RoleContextRemoveRole API
uid: UID, the unified identifier for each user account
Returns:
Response containing Definition of Delete Request
18.13
"""
if(self._lr_object.is_null_or_whitespace(context_name)):
raise Exception(self._lr_object.get_validation_message("context_name"))
if(role_context_remove_role_model is None):
raise Exception(self._lr_object.get_validation_message("role_context_remove_role_model"))
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/rolecontext/" + context_name + "/role"
return self._lr_object.execute("DELETE", resource_path, query_parameters, role_context_remove_role_model)
def delete_additional_permission_from_role_context_by_uid(self, context_name, role_context_additional_permission_remove_role_model, uid):
"""This API Deletes Additional Permissions from Context.
Args:
context_name: Name of context
role_context_additional_permission_remove_role_model: Model Class containing Definition of payload for RoleContextAdditionalPermissionRemoveRole API
uid: UID, the unified identifier for each user account
Returns:
Response containing Definition of Delete Request
18.14
"""
if(self._lr_object.is_null_or_whitespace(context_name)):
raise Exception(self._lr_object.get_validation_message("context_name"))
if(role_context_additional_permission_remove_role_model is None):
raise Exception(self._lr_object.get_validation_message("role_context_additional_permission_remove_role_model"))
if(self._lr_object.is_null_or_whitespace(uid)):
raise Exception(self._lr_object.get_validation_message("uid"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/account/" + uid + "/rolecontext/" + context_name + "/additionalpermission"
return self._lr_object.execute("DELETE", resource_path, query_parameters, role_context_additional_permission_remove_role_model)
def get_roles_list(self):
"""This API retrieves the complete list of created roles with permissions of your app.
Returns:
Complete user Roles List data
41.1
"""
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/role"
return self._lr_object.execute("GET", resource_path, query_parameters, None)
def create_roles(self, roles_model):
"""This API creates a role with permissions.
Args:
roles_model: Model Class containing Definition of payload for Roles API
Returns:
Complete user Roles data
41.2
"""
if(roles_model is None):
raise Exception(self._lr_object.get_validation_message("roles_model"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/role"
return self._lr_object.execute("POST", resource_path, query_parameters, roles_model)
def delete_role(self, role):
"""This API is used to delete the role.
Args:
role: Created RoleName
Returns:
Response containing Definition of Delete Request
41.3
"""
if(self._lr_object.is_null_or_whitespace(role)):
raise Exception(self._lr_object.get_validation_message("role"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/role/" + role
return self._lr_object.execute("DELETE", resource_path, query_parameters, None)
def add_role_permissions(self, permissions_model, role):
"""This API is used to add permissions to a given role.
Args:
permissions_model: Model Class containing Definition for PermissionsModel Property
role: Created RoleName
Returns:
Response containing Definition of Complete role data
41.4
"""
if(permissions_model is None):
raise Exception(self._lr_object.get_validation_message("permissions_model"))
if(self._lr_object.is_null_or_whitespace(role)):
raise Exception(self._lr_object.get_validation_message("role"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/role/" + role + "/permission"
return self._lr_object.execute("PUT", resource_path, query_parameters, permissions_model)
def remove_role_permissions(self, permissions_model, role):
"""API is used to remove permissions from a role.
Args:
permissions_model: Model Class containing Definition for PermissionsModel Property
role: Created RoleName
Returns:
Response containing Definition of Complete role data
41.5
"""
if(permissions_model is None):
raise Exception(self._lr_object.get_validation_message("permissions_model"))
if(self._lr_object.is_null_or_whitespace(role)):
raise Exception(self._lr_object.get_validation_message("role"))
query_parameters = {}
query_parameters["apiKey"] = self._lr_object.get_api_key()
query_parameters["apiSecret"] = self._lr_object.get_api_secret()
resource_path = "identity/v2/manage/role/" + role + "/permission"
return self._lr_object.execute("DELETE", resource_path, query_parameters, permissions_model)
| 40.610778
| 160
| 0.674801
| 1,643
| 13,564
| 5.225806
| 0.080949
| 0.078267
| 0.114605
| 0.089099
| 0.863382
| 0.850338
| 0.821803
| 0.786047
| 0.773119
| 0.756348
| 0
| 0.006402
| 0.2399
| 13,564
| 333
| 161
| 40.732733
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| 1
| 0.112782
| false
| 0
| 0
| 0
| 0.225564
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 1
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| null | 0
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| 0
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| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
e9078b1fbf4f73d1d7c75520ee2ca0bf24e3107d
| 3,839
|
py
|
Python
|
dnn_initialization.py
|
alebac/Deep-Neural-Net
|
47d704466e8e880457f9298403a0f4ea0fd3f7cd
|
[
"MIT"
] | null | null | null |
dnn_initialization.py
|
alebac/Deep-Neural-Net
|
47d704466e8e880457f9298403a0f4ea0fd3f7cd
|
[
"MIT"
] | 6
|
2020-09-21T14:54:09.000Z
|
2020-09-21T15:01:52.000Z
|
dnn_initialization.py
|
alebac/Deep-Neural-Net
|
47d704466e8e880457f9298403a0f4ea0fd3f7cd
|
[
"MIT"
] | null | null | null |
import numpy as np
def initialize_parameters_zeros(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
bl -- bias vector of shape (layer_dims[l], 1)
"""
np.random.seed(1)
parameters = {}
L = len(layer_dims) # number of layers in the network
for l in range(1, L):
parameters['W' + str(l)] = np.zeros((layer_dims[l], layer_dims[l-1])) #*0.01
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))
assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))
return parameters
def initialize_parameters_random(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
bl -- bias vector of shape (layer_dims[l], 1)
"""
np.random.seed(1)
parameters = {}
L = len(layer_dims) # number of layers in the network
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) *0.01
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))
assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))
return parameters
def initialize_parameters_he(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
bl -- bias vector of shape (layer_dims[l], 1)
"""
np.random.seed(1)
parameters = {}
L = len(layer_dims) # number of layers in the network
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) * np.sqrt(2/layer_dims[l-1]) #*0.01
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))
assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))
return parameters
def initialize_parameters_deep(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
bl -- bias vector of shape (layer_dims[l], 1)
"""
np.random.seed(1)
parameters = {}
L = len(layer_dims) # number of layers in the network
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) / np.sqrt(layer_dims[l-1]) #*0.01
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))
assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))
return parameters
| 38.009901
| 119
| 0.560302
| 516
| 3,839
| 4.056202
| 0.106589
| 0.215002
| 0.181558
| 0.136646
| 0.973244
| 0.973244
| 0.973244
| 0.970855
| 0.970855
| 0.970855
| 0
| 0.020206
| 0.290961
| 3,839
| 101
| 120
| 38.009901
| 0.748714
| 0.389424
| 0
| 0.780488
| 0
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| 0.007685
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| 0.195122
| 1
| 0.097561
| false
| 0
| 0.02439
| 0
| 0.219512
| 0
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| 0
| null | 1
| 1
| 0
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| 1
| 1
| 1
| 1
| 1
| 0
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| 0
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| 0
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| 0
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| null | 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
c92b2a6fba4678e91949130974dbe37084615fef
| 178
|
py
|
Python
|
rpython/translator/cli/test/test_cast.py
|
kantai/passe-pypy-taint-tracking
|
b60a3663f8fe89892dc182c8497aab97e2e75d69
|
[
"MIT"
] | 2
|
2016-07-06T23:30:20.000Z
|
2017-05-30T15:59:31.000Z
|
rpython/translator/cli/test/test_cast.py
|
kantai/passe-pypy-taint-tracking
|
b60a3663f8fe89892dc182c8497aab97e2e75d69
|
[
"MIT"
] | null | null | null |
rpython/translator/cli/test/test_cast.py
|
kantai/passe-pypy-taint-tracking
|
b60a3663f8fe89892dc182c8497aab97e2e75d69
|
[
"MIT"
] | 2
|
2020-07-09T08:14:22.000Z
|
2021-01-15T18:01:25.000Z
|
from rpython.translator.cli.test.runtest import CliTest
from rpython.translator.oosupport.test_template.cast import BaseTestCast
class TestCast(BaseTestCast, CliTest):
pass
| 29.666667
| 72
| 0.837079
| 22
| 178
| 6.727273
| 0.681818
| 0.148649
| 0.283784
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095506
| 178
| 5
| 73
| 35.6
| 0.919255
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| true
| 0.25
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| null | 0
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| 1
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| 1
| 0
|
0
| 7
|
c96e2f7da08aac7f0371d29837c9f7c72cf360c9
| 2,656
|
py
|
Python
|
Incident-Response/Tools/dfirtrack/dfirtrack_artifacts/migrations/0002_default_values.py
|
sn0b4ll/Incident-Playbook
|
cf519f58fcd4255674662b3620ea97c1091c1efb
|
[
"MIT"
] | 1
|
2021-07-24T17:22:50.000Z
|
2021-07-24T17:22:50.000Z
|
Incident-Response/Tools/dfirtrack/dfirtrack_artifacts/migrations/0002_default_values.py
|
sn0b4ll/Incident-Playbook
|
cf519f58fcd4255674662b3620ea97c1091c1efb
|
[
"MIT"
] | 2
|
2022-02-28T03:40:31.000Z
|
2022-02-28T03:40:52.000Z
|
Incident-Response/Tools/dfirtrack/dfirtrack_artifacts/migrations/0002_default_values.py
|
sn0b4ll/Incident-Playbook
|
cf519f58fcd4255674662b3620ea97c1091c1efb
|
[
"MIT"
] | 2
|
2022-02-25T08:34:51.000Z
|
2022-03-16T17:29:44.000Z
|
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('dfirtrack_artifacts', '0001_initial'),
]
operations = [
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('10_needs_analysis', '10_needs_analysis');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('20_requested', '20_requested');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('21_requested_again', '21_requested_again');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('25_collection_ongoing', '25_collection_ongoing');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('30_processing_ongoing', '30_processing_ongoing');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('40_import_ongoing', '40_import_ongoing');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('50_ready_for_analysis', '50_ready_for_analysis');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('60_analysis_ongoing', '60_analysis_ongoing');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('70_analysis_finished', '70_analysis_finished');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('90_not_analyzed', '90_not_analyzed');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifactstatus (artifactstatus_name, artifactstatus_slug) VALUES ('95_not_available', '95_not_available');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifacttype (artifacttype_name, artifacttype_slug) VALUES ('File', 'file');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifacttype (artifacttype_name, artifacttype_slug) VALUES ('Image', 'image');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifacttype (artifacttype_name, artifacttype_slug) VALUES ('Information', 'information');"),
migrations.RunSQL("INSERT INTO dfirtrack_artifacts_artifacttype (artifacttype_name, artifacttype_slug) VALUES ('Triage', 'triage');"),
]
| 91.586207
| 178
| 0.789157
| 269
| 2,656
| 7.39777
| 0.182156
| 0.144724
| 0.165829
| 0.19598
| 0.763819
| 0.763819
| 0.763819
| 0.763819
| 0.763819
| 0.763819
| 0
| 0.020253
| 0.107681
| 2,656
| 28
| 179
| 94.857143
| 0.819409
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| 0
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| 0
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| 0.769202
| 0.416039
| 0
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| 0
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| 1
| 0
| false
| 0
| 0.090909
| 0
| 0.227273
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| null | 0
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| 1
| 1
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 8
|
a30b2f69edae47da204618c2e158853aa0d16126
| 2,942
|
py
|
Python
|
avicena/optimizers/solver_util/cplex/Listeners.py
|
ribsthakkar/RiderPickup
|
e4eaf69d905631034949b18433bc70e4a08f0e58
|
[
"MIT"
] | 2
|
2020-04-21T02:43:23.000Z
|
2020-04-21T02:44:44.000Z
|
avicena/optimizers/solver_util/cplex/Listeners.py
|
ribsthakkar/RiderPickup
|
e4eaf69d905631034949b18433bc70e4a08f0e58
|
[
"MIT"
] | 1
|
2020-04-21T02:41:11.000Z
|
2020-04-21T02:41:11.000Z
|
avicena/optimizers/solver_util/cplex/Listeners.py
|
ribsthakkar/RiderPickup
|
e4eaf69d905631034949b18433bc70e4a08f0e58
|
[
"MIT"
] | null | null | null |
from docplex.mp.progress import ProgressListener, ProgressData
import logging
log = logging.getLogger(__name__)
class TimeListener(ProgressListener):
"""
Sample CPLEX Listener found on IBM DoCPLEX Forums. This listener logs and tracks MIP Gap and the time passed
in attempt to solve the problem. It aborts the solve if a certain amount of time has passed.
"""
def __init__(self, time: int):
"""
Initalize Listener
:param time: time in seconds until the solve attempt will end
"""
ProgressListener.__init__(self)
self._time = time
def notify_progress(self, data: ProgressData) -> None:
"""
A Callback used by the CPLEX solver to update the listener on the progress so far on the solution.
:param data: ProgressData struct with details about the solution's progress
"""
log.info('Elapsed time: %.2f' % data.time)
if data.has_incumbent:
log.info('Current incumbent: %f' % data.current_objective)
log.info('Current gap: %.2f%%' % (100. * data.mip_gap))
# If we are solving for longer than the specified time then
# stop if we reach the predefined alternate MIP gap.
if data.time > self._time:
log.info('ABORTING')
self.abort()
elif data.time > self._time:
self.abort()
else:
log.info('No feasible solution found yet')
pass
class GapListener(ProgressListener):
"""
Sample CPLEX Listener found on IBM DoCPLEX Forums. This listener logs and tracks MIP Gap and the time passed
in attempt to solve the problem. It aborts the solve if a certain MIP gap is reached or a certain amount of time has
passed.
"""
def __init__(self, time: int, gap: float) -> None:
"""
Initialize Listener
:param time: time in seconds until the solve attempt will end
:param gap: target MIP gap
"""
ProgressListener.__init__(self)
self._time = time
self._gap = gap
def notify_progress(self, data: ProgressData) -> None:
"""
A Callback used by the CPLEX solver to update the listener on the progress so far on the solution.
:param data: ProgressData struct with details about the solution's progress
"""
log.info('Elapsed time: %.2f' % data.time)
if data.has_incumbent:
log.info('Current incumbent: %f' % data.current_objective)
log.info('Current gap: %.2f%%' % (100. * data.mip_gap))
# If we are solving for longer than the specified time then
# stop if we reach the predefined alternate MIP gap.
if data.time > self._time or data.mip_gap < self._gap:
log.info('ABORTING')
self.abort()
else:
log.info('No feasible solution yet')
pass
| 38.207792
| 120
| 0.618287
| 379
| 2,942
| 4.704485
| 0.271768
| 0.03926
| 0.031408
| 0.026921
| 0.854178
| 0.832305
| 0.791924
| 0.791924
| 0.749299
| 0.749299
| 0
| 0.004869
| 0.301835
| 2,942
| 76
| 121
| 38.710526
| 0.863194
| 0.405846
| 0
| 0.638889
| 0
| 0
| 0.118698
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0.055556
| 0.055556
| 0
| 0.222222
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
a3770c0b42e5c440fa7938e5227477491d41b71e
| 5,874
|
py
|
Python
|
tests/test_ciw_helper.py
|
pp81381/nicett6
|
addace8fbd5350105bf4fb27d1b485bb9cf20236
|
[
"MIT"
] | null | null | null |
tests/test_ciw_helper.py
|
pp81381/nicett6
|
addace8fbd5350105bf4fb27d1b485bb9cf20236
|
[
"MIT"
] | 1
|
2021-06-06T20:43:09.000Z
|
2021-06-06T20:43:09.000Z
|
tests/test_ciw_helper.py
|
pp81381/nicett6
|
addace8fbd5350105bf4fb27d1b485bb9cf20236
|
[
"MIT"
] | null | null | null |
from nicett6.ciw_helper import CIWHelper, ImageDef
from nicett6.cover import Cover
from unittest import IsolatedAsyncioTestCase, TestCase
class TestImageDef(TestCase):
def setUp(self):
self.image_def = ImageDef(0.05, 1.8, 16 / 9)
def tearDown(self) -> None:
self.image_def = None
def test1(self):
"""Test width"""
self.assertAlmostEqual(self.image_def.width, 3.2)
def test2(self):
"""Test 2.35"""
self.assertAlmostEqual(self.image_def.implied_image_height(2.35), 1.361702128)
def test3(self):
"""Test 16 / 9"""
self.assertAlmostEqual(self.image_def.implied_image_height(16 / 9), 1.8)
def test4(self):
"""Test capping of implied image height"""
self.assertAlmostEqual(self.image_def.implied_image_height(4 / 3), 1.8)
def test5(self):
"""Test 16 / 9 ish capped"""
self.assertAlmostEqual(self.image_def.implied_image_height(1.77), 1.8)
def test6(self):
"""Test 16 / 9 ish not capped"""
self.assertAlmostEqual(self.image_def.implied_image_height(1.78), 1.7977528)
def test7(self):
"""Test invalid target aspect ratio low"""
with self.assertRaises(ValueError):
self.image_def.implied_image_height(0.2)
def test8(self):
"""Test invalid target aspect ratio high"""
with self.assertRaises(ValueError):
self.image_def.implied_image_height(4)
class TestCIWHelper(IsolatedAsyncioTestCase):
def setUp(self):
image_def = ImageDef(0.05, 1.8, 16 / 9)
self.helper = CIWHelper(Cover("Screen", 2.0), Cover("Mask", 0.8), image_def)
def tearDown(self) -> None:
self.helper = None
async def test1(self):
"""Screen fully up, mask fully up"""
self.assertAlmostEqual(self.helper.screen.max_drop, 2.0)
self.assertAlmostEqual(self.helper.image_width, 3.2)
self.assertAlmostEqual(self.helper.screen.drop_pct, 1.0)
self.assertAlmostEqual(self.helper.screen.drop, 0.0)
self.assertAlmostEqual(self.helper.mask.drop_pct, 1.0)
self.assertAlmostEqual(self.helper.mask.drop, 0.0)
self.assertEqual(self.helper.image_is_visible, False)
self.assertAlmostEqual(self.helper.image_height, None)
self.assertAlmostEqual(self.helper.aspect_ratio, None)
self.assertAlmostEqual(self.helper.image_diagonal, None)
self.assertAlmostEqual(self.helper.image_area, None)
async def test2(self):
"""Screen fully down, mask fully up"""
await self.helper.screen.set_drop_pct(0.0)
self.assertAlmostEqual(self.helper.screen.max_drop, 2.0)
self.assertAlmostEqual(self.helper.image_width, 3.2)
self.assertAlmostEqual(self.helper.screen.drop_pct, 0.0)
self.assertAlmostEqual(self.helper.screen.drop, 2.0)
self.assertAlmostEqual(self.helper.mask.drop_pct, 1.0)
self.assertAlmostEqual(self.helper.mask.drop, 0.0)
self.assertEqual(self.helper.image_is_visible, True)
self.assertAlmostEqual(self.helper.image_height, 1.8)
self.assertAlmostEqual(self.helper.aspect_ratio, 16.0 / 9.0)
self.assertAlmostEqual(self.helper.image_diagonal, 3.67151195)
self.assertAlmostEqual(self.helper.image_area, 5.76)
async def test3(self):
"""Screen fully up, mask fully down"""
await self.helper.mask.set_drop_pct(0.0)
self.assertAlmostEqual(self.helper.screen.max_drop, 2.0)
self.assertAlmostEqual(self.helper.image_width, 3.2)
self.assertAlmostEqual(self.helper.screen.drop_pct, 1.0)
self.assertAlmostEqual(self.helper.screen.drop, 0.0)
self.assertAlmostEqual(self.helper.mask.drop_pct, 0.0)
self.assertAlmostEqual(self.helper.mask.drop, 0.8)
self.assertEqual(self.helper.image_is_visible, False)
self.assertAlmostEqual(self.helper.image_height, None)
self.assertAlmostEqual(self.helper.aspect_ratio, None)
self.assertAlmostEqual(self.helper.image_diagonal, None)
self.assertAlmostEqual(self.helper.image_area, None)
async def test4(self):
"""Screen hiding top border, mask fully up"""
await self.helper.screen.set_drop_pct(0.15 / 2.0)
self.assertAlmostEqual(self.helper.screen.max_drop, 2.0)
self.assertAlmostEqual(self.helper.image_width, 3.2)
self.assertAlmostEqual(self.helper.screen.drop_pct, 0.15 / 2.0)
self.assertAlmostEqual(self.helper.screen.drop, 1.85)
self.assertAlmostEqual(self.helper.mask.drop_pct, 1.0)
self.assertAlmostEqual(self.helper.mask.drop, 0.0)
self.assertEqual(self.helper.image_is_visible, True)
self.assertAlmostEqual(self.helper.image_height, 1.8)
self.assertAlmostEqual(self.helper.aspect_ratio, 16.0 / 9.0)
self.assertAlmostEqual(self.helper.image_diagonal, 3.67151195)
self.assertAlmostEqual(self.helper.image_area, 5.76)
async def test5(self):
"""Screen fully down, mask set for 2.35 absolute"""
await self.helper.screen.set_drop_pct(0.0)
await self.helper.mask.set_drop_pct(0.26462766)
self.assertAlmostEqual(self.helper.screen.max_drop, 2.0)
self.assertAlmostEqual(self.helper.image_width, 3.2)
self.assertAlmostEqual(self.helper.screen.drop_pct, 0.0)
self.assertAlmostEqual(self.helper.screen.drop, 2.0)
self.assertAlmostEqual(self.helper.mask.drop_pct, 0.26462766)
self.assertAlmostEqual(self.helper.mask.drop, 0.588297872)
self.assertEqual(self.helper.image_is_visible, True)
self.assertAlmostEqual(self.helper.image_height, 1.361702128)
self.assertAlmostEqual(self.helper.aspect_ratio, 2.35)
self.assertAlmostEqual(self.helper.image_diagonal, 3.477676334)
self.assertAlmostEqual(self.helper.image_area, 4.35744681)
| 45.184615
| 86
| 0.691862
| 782
| 5,874
| 5.085678
| 0.118926
| 0.155896
| 0.352024
| 0.389741
| 0.853156
| 0.814182
| 0.764395
| 0.749309
| 0.676389
| 0.667589
| 0
| 0.054984
| 0.188798
| 5,874
| 129
| 87
| 45.534884
| 0.779643
| 0.033027
| 0
| 0.546392
| 0
| 0
| 0.001841
| 0
| 0
| 0
| 0
| 0
| 0.649485
| 1
| 0.123711
| false
| 0
| 0.030928
| 0
| 0.175258
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
6e6debe47dc9be748bb5c509d0d7d0632b908832
| 116
|
py
|
Python
|
morpfw/authn/pas/model.py
|
morpframework/morpfw
|
b867e5809d6c52e8839586670a29fcd179ce64c7
|
[
"Apache-2.0"
] | 8
|
2018-12-08T01:41:58.000Z
|
2020-12-21T15:30:12.000Z
|
morpfw/authn/pas/model.py
|
morpframework/morpfw
|
b867e5809d6c52e8839586670a29fcd179ce64c7
|
[
"Apache-2.0"
] | 17
|
2019-02-05T15:01:32.000Z
|
2020-04-28T16:17:42.000Z
|
morpfw/authn/pas/model.py
|
morpframework/morpfw
|
b867e5809d6c52e8839586670a29fcd179ce64c7
|
[
"Apache-2.0"
] | 2
|
2018-12-08T05:03:37.000Z
|
2019-03-20T07:15:21.000Z
|
import re
NAME_PATTERN = r'^[a-z0-9_@:\.]+$'
EMAIL_PATTERN = r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)"
| 23.2
| 69
| 0.508621
| 23
| 116
| 2.391304
| 0.434783
| 0.218182
| 0.272727
| 0.327273
| 0.327273
| 0.327273
| 0.327273
| 0.327273
| 0
| 0
| 0
| 0.074766
| 0.077586
| 116
| 4
| 70
| 29
| 0.439252
| 0
| 0
| 0
| 0
| 0.333333
| 0.568966
| 0.431034
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 9
|
6e79d4384e088aeac845e9450ffc42dcf4713630
| 18,327
|
py
|
Python
|
ckanext/issues/tests/controllers/test_spam.py
|
rhabbachi/ckanext-issues
|
d8c3dde8372e88dd5dc173023df34c90034ca777
|
[
"MIT"
] | 8
|
2016-06-16T20:45:54.000Z
|
2020-09-24T12:06:15.000Z
|
ckanext/issues/tests/controllers/test_spam.py
|
rhabbachi/ckanext-issues
|
d8c3dde8372e88dd5dc173023df34c90034ca777
|
[
"MIT"
] | 50
|
2015-03-25T16:59:11.000Z
|
2016-01-10T21:35:26.000Z
|
ckanext/issues/tests/controllers/test_spam.py
|
rhabbachi/ckanext-issues
|
d8c3dde8372e88dd5dc173023df34c90034ca777
|
[
"MIT"
] | 11
|
2016-09-14T13:34:53.000Z
|
2020-08-28T05:48:58.000Z
|
from cStringIO import StringIO
import bs4
from ckan import model
from ckan.plugins import toolkit
try:
from ckan.new_tests import helpers
from ckan.new_tests import factories
except ImportError:
from ckan.tests import helpers
from ckan.tests import factories
from ckanext.issues.model import Issue, IssueComment, AbuseStatus
from ckanext.issues.tests import factories as issue_factories
from ckanext.issues.tests.helpers import ClearOnTearDownMixin
from lxml import etree
from nose.tools import assert_equals, assert_in, assert_not_in
class TestModeratedAbuseReport(helpers.FunctionalTestBase):
def setup(self):
super(TestModeratedAbuseReport, self).setup()
self.owner = factories.User()
self.reporter = factories.User()
self.org = factories.Organization(user=self.owner)
self.dataset = factories.Dataset(user=self.owner,
owner_org=self.org['name'])
# issue_abuse is moderated - i.e. definitely abuse/spam
self.issue_abuse = issue_factories.Issue(
user=self.owner,
user_id=self.owner['id'],
dataset_id=self.dataset['id'])
issue_abuse = Issue.get(self.issue_abuse['id'])
issue_abuse.visibility = 'hidden'
issue_abuse.report_abuse(model.Session, self.reporter['id'])
issue_abuse.abuse_status = AbuseStatus.abuse.value
issue_abuse.save()
self.user = factories.User()
self.app = self._get_test_app()
def test_abuse_label_appears_for_admin(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_show',
dataset_id=self.dataset['id'],
issue_number=self.issue_abuse['number']),
extra_environ=env,
)
res_chunks = parse_issues_show(response)
assert_in('Test Issue', res_chunks['issue_name'])
assert_in('Hidden from normal users', res_chunks['issue_comment_label'])
assert_in('Moderated: abuse', res_chunks['issue_comment_label'])
assert_in('1 user reports this is spam/abus', res_chunks['issue_comment_label'])
assert_in(self.reporter['name'], res_chunks['issue_comment_label'])
def test_reported_as_abuse_appears_in_search_as_admin(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_dataset',
dataset_id=self.dataset['id']),
extra_environ=env,
)
res_chunks = parse_issues_dataset(response)
assert_in('1 issue found', res_chunks['issues_found'])
assert_in('Test Issue', res_chunks['issue_name'])
assert_in('Spam/Abuse - hidden from normal users', res_chunks['issue_comment_label'])
def test_reported_as_abuse_does_not_appear_in_search_to_user_who_reported_it(self):
env = {'REMOTE_USER': self.reporter['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_dataset',
dataset_id=self.dataset['id']),
extra_environ=env,
)
res_chunks = parse_issues_dataset(response)
assert_in('0 issues found', res_chunks['issues_found'])
def test_reported_as_abuse_does_not_appear_as_non_admin(self):
env = {'REMOTE_USER': self.user['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_dataset',
dataset_id=self.dataset['id']),
extra_environ=env,
)
res_chunks = parse_issues_dataset(response)
assert_in('0 issues found', res_chunks['issues_found'])
assert_not_in('Spam', res_chunks['issue_comment_label'])
class TestUnmoderatedAbuseReport(helpers.FunctionalTestBase):
def setup(self):
super(TestUnmoderatedAbuseReport, self).setup()
self.owner = factories.User()
self.reporter = factories.User()
self.org = factories.Organization(user=self.owner)
self.dataset = factories.Dataset(user=self.owner,
owner_org=self.org['name'])
# issue_reported is reported by a user but not moderated - i.e. may be
# abuse/spam but it is still visible
self.issue_reported = issue_factories.Issue(
user=self.owner,
user_id=self.owner['id'],
dataset_id=self.dataset['id'])
issue_reported = Issue.get(self.issue_reported['id'])
issue_reported.visibility = 'visible'
issue_reported.report_abuse(model.Session, self.reporter['id'])
issue_reported.abuse_status = AbuseStatus.unmoderated.value
issue_reported.save()
self.user = factories.User()
self.app = self._get_test_app()
def test_abuse_label_appears_for_admin(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_show',
dataset_id=self.dataset['id'],
issue_number=self.issue_reported['number']),
extra_environ=env,
)
res_chunks = parse_issues_show(response)
assert_in('Test Issue', res_chunks['issue_name'])
assert_not_in('Hidden from normal users', res_chunks['issue_comment_label'])
assert_not_in('Moderated', res_chunks['issue_comment_label'])
assert_in('1 user reports this is spam/abuse', res_chunks['issue_comment_label'])
assert_in(self.reporter['name'], res_chunks['issue_comment_label'])
def test_reported_as_abuse_appears_in_search_as_admin(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_dataset',
dataset_id=self.dataset['id']),
extra_environ=env,
)
res_chunks = parse_issues_dataset(response)
assert_in('1 issue found', res_chunks['issues_found'])
assert_in('Test Issue', res_chunks['issue_name'])
assert_not_in('Spam/Abuse', res_chunks['issue_comment_label'])
# Would be good if it said it had reports though
def test_reported_as_abuse_appears_in_search_to_user_who_reported_it(self):
env = {'REMOTE_USER': self.reporter['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_dataset',
dataset_id=self.dataset['id']),
extra_environ=env,
)
res_chunks = parse_issues_dataset(response)
assert_in('1 issue found', res_chunks['issues_found'])
assert_in('Test Issue', res_chunks['issue_name'])
assert_in('Reported by you to admins', res_chunks['issue_comment_label'])
def test_reported_as_abuse_appears_as_non_admin(self):
env = {'REMOTE_USER': self.user['name'].encode('ascii')}
response = self.app.get(
url=toolkit.url_for('issues_dataset',
dataset_id=self.dataset['id']),
extra_environ=env,
)
res_chunks = parse_issues_dataset(response)
assert_in('1 issue found', res_chunks['issues_found'])
assert_in('Test Issue', res_chunks['issue_name'])
assert_not_in('Spam', res_chunks['issue_comment_label'])
def pprint_html(trees):
return '\n'.join([etree.tostring(tree, pretty_print=True).strip()
for tree in trees])
def primary_div(tree):
# This xpath looks for 'primary' as a whole word
return tree.xpath(
"//div[contains(concat(' ', normalize-space(@class), ' '), ' primary ')]")[0]
def parse_issues_dataset(response):
'''Given the response from a GET url_for issues_dataset,
returns named chunks of it that can be tested.
'''
tree = etree.parse(StringIO(response.body), parser=etree.HTMLParser())
primary_tree = primary_div(tree)
return {
'primary_tree': pprint_html(primary_tree),
'issue_comment_label': pprint_html(primary_tree.xpath('//div[@class="issue-comment-label"]')),
'issue_name': pprint_html(primary_tree.xpath('//h4[@class="list-group-item-name"]')),
'issues_found': pprint_html(primary_tree.xpath('//h2[@id="issues-found"]')),
}
def parse_issues_show(response):
'''Given the response from a GET url_for issues_show,
returns named chunks of it that can be tested.
'''
tree = etree.parse(StringIO(response.body), parser=etree.HTMLParser())
primary_tree = primary_div(tree)
return {
'primary_tree': pprint_html(primary_tree),
'issue_comment_label': pprint_html(primary_tree.xpath('//div[@class="issue-comment-label"]')),
'issue_comment_action': pprint_html(primary_tree.xpath('//div[@class="issue-comment-action"]')),
'issue_name': pprint_html(primary_tree.xpath('//h1[@class="page-heading"]')),
}
class TestReportIssue(helpers.FunctionalTestBase):
def setup(self):
super(TestReportIssue, self).setup()
self.joe_public = factories.User()
self.owner = factories.User()
self.org = factories.Organization(user=self.owner)
self.dataset = factories.Dataset(user=self.owner,
owner_org=self.org['name'])
self.issue = issue_factories.Issue(user=self.owner,
user_id=self.owner['id'],
dataset_id=self.dataset['id'])
self.app = self._get_test_app()
def test_report(self):
env = {'REMOTE_USER': self.joe_public['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_report',
dataset_id=self.dataset['id'],
issue_number=self.issue['number']),
extra_environ=env,
)
response = response.follow()
soup = bs4.BeautifulSoup(response.body)
flash_messages = soup.find('div', {'class': 'flash-messages'}).text
assert_in('Issue reported to an administrator', flash_messages)
def test_report_as_admin(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_report',
dataset_id=self.dataset['id'],
issue_number=self.issue['number']),
extra_environ=env,
)
response = response.follow(extra_environ=env)
soup = bs4.BeautifulSoup(response.body)
flash_messages = soup.find('div', {'class': 'flash-messages'}).text
assert_in('Report acknowledged. Marked as abuse/spam. '
'Issue is invisible to normal users.', flash_messages)
def test_report_as_anonymous_user(self):
response = self.app.post(
url=toolkit.url_for('issues_report',
dataset_id=self.dataset['id'],
issue_number=self.issue['number']),
)
response = response.follow()
assert_in('You must be logged in to report issues',
response.body)
def test_report_an_issue_that_does_not_exist(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_report',
dataset_id=self.dataset['id'],
issue_number='1235455'),
extra_environ=env,
expect_errors=True
)
assert_equals(response.status_int, 404)
def test_report_clear(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_report_clear',
dataset_id=self.dataset['id'],
issue_number=self.issue['number']),
extra_environ=env,
)
response = response.follow()
assert_in('Issue report cleared', response.body)
def test_report_clear_normal_user(self):
user = factories.User()
model.Session.add(Issue.Report(user['id'],
self.issue['id']))
model.Session.commit()
env = {'REMOTE_USER': user['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_report_clear',
dataset_id=self.dataset['id'],
issue_number=self.issue['number']),
extra_environ=env,
expect_errors=True
)
response = response.follow()
assert_in('Issue report cleared', response.body)
def test_reset_on_issue_that_does_not_exist(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_report_clear',
dataset_id=self.dataset['id'],
issue_number='1235455'),
extra_environ=env,
expect_errors=True
)
assert_equals(response.status_int, 404)
class TestReportComment(helpers.FunctionalTestBase):
def setup(self):
super(TestReportComment, self).setup()
self.joe_public = factories.User()
self.owner = factories.User()
self.org = factories.Organization(user=self.owner)
self.dataset = factories.Dataset(user=self.owner,
owner_org=self.org['name'])
self.issue = issue_factories.Issue(user=self.owner,
user_id=self.owner['id'],
dataset_id=self.dataset['id'])
self.comment = issue_factories.IssueComment(
user_id=self.owner['id'],
dataset_id=self.dataset['id'],
issue_number=self.issue['number']
)
self.app = self._get_test_app()
def test_report(self):
env = {'REMOTE_USER': self.joe_public['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_comment_report',
dataset_id=self.dataset['id'],
issue_number=self.issue['number'],
comment_id=self.comment['id']),
extra_environ=env,
)
response = response.follow()
soup = bs4.BeautifulSoup(response.body)
flash_messages = soup.find('div', {'class': 'flash-messages'}).text
assert_in('Comment has been reported to an administrator',
flash_messages)
def test_report_as_admin(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_comment_report',
dataset_id=self.dataset['id'],
issue_number=self.issue['number'],
comment_id=self.comment['id']),
extra_environ=env,
)
response = response.follow()
soup = bs4.BeautifulSoup(response.body)
flash_messages = soup.find('div', {'class': 'flash-messages'}).text
assert_in('Report acknowledged. Marked as abuse/spam. '
'Comment is invisible to normal users.', flash_messages)
def test_report_not_logged_in(self):
response = self.app.post(
url=toolkit.url_for('issues_comment_report',
dataset_id=self.dataset['id'],
issue_number=self.issue['number'],
comment_id=self.comment['id']),
)
response = response.follow()
assert_in('You must be logged in to report comments',
response.body)
def test_report_an_issue_that_does_not_exist(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_comment_report',
dataset_id=self.dataset['id'],
issue_number='1235455',
comment_id=self.comment['id']),
extra_environ=env,
expect_errors=True
)
assert_equals(response.status_int, 404)
def test_report_clear(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_comment_report_clear',
dataset_id=self.dataset['id'],
issue_number=self.issue['number'],
comment_id=self.comment['id']),
extra_environ=env,
)
response = response.follow()
assert_in('Spam/abuse report cleared', response.body)
def test_report_clear_state_normal_user(self):
user = factories.User()
model.Session.add(IssueComment.Report(user['id'], self.comment['id']))
model.Session.commit()
env = {'REMOTE_USER': user['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_comment_report_clear',
dataset_id=self.dataset['id'],
issue_number=self.issue['number'],
comment_id=self.comment['id']),
extra_environ=env,
)
response = response.follow()
assert_in('Spam/abuse report cleared', response.body)
def test_reset_on_issue_that_does_not_exist(self):
env = {'REMOTE_USER': self.owner['name'].encode('ascii')}
response = self.app.post(
url=toolkit.url_for('issues_comment_report_clear',
dataset_id=self.dataset['id'],
issue_number='1235455',
comment_id='12312323'),
extra_environ=env,
expect_errors=True
)
assert_equals(response.status_int, 404)
| 43.844498
| 104
| 0.593114
| 2,084
| 18,327
| 4.965931
| 0.09261
| 0.046961
| 0.036429
| 0.052179
| 0.848005
| 0.838052
| 0.819789
| 0.81032
| 0.790415
| 0.768577
| 0
| 0.004986
| 0.288645
| 18,327
| 417
| 105
| 43.94964
| 0.788832
| 0.024609
| 0
| 0.709141
| 0
| 0
| 0.147509
| 0.022642
| 0
| 0
| 0
| 0
| 0.110803
| 1
| 0.083102
| false
| 0
| 0.038781
| 0.00554
| 0.144044
| 0.027701
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6edafe2cc0575b8df78491cbf3b3d98b8b79282a
| 4,423
|
py
|
Python
|
tests/test_gene_region_annotators.py
|
IMTMarburg/mbf_genomics
|
b83e318dbcd487ade846a6bf6f17724833f85797
|
[
"MIT"
] | null | null | null |
tests/test_gene_region_annotators.py
|
IMTMarburg/mbf_genomics
|
b83e318dbcd487ade846a6bf6f17724833f85797
|
[
"MIT"
] | null | null | null |
tests/test_gene_region_annotators.py
|
IMTMarburg/mbf_genomics
|
b83e318dbcd487ade846a6bf6f17724833f85797
|
[
"MIT"
] | null | null | null |
# flake8: noqa
if False:
import pytest
import mbf_genomics.genes as genes
@pytest.mark.usefixtures("new_pipegraph")
class TestRegionAnnotationWithGenes:
def test_anno_next_genes(self):
genome = DummyGenome(
pd.DataFrame(
[
{
"stable_id": "fake1",
"chr": "1",
"strand": 1,
"tss": 5000,
"tes": 5500,
"description": "bla",
},
{
"stable_id": "fake2",
"chr": "1",
"strand": -1,
"tss": 5400,
"tes": 4900,
"description": "bla",
},
{
"stable_id": "fake3",
"chr": "2",
"strand": -1,
"tss": 5400,
"tes": 4900,
"description": "bla",
},
]
)
)
def sample_data():
df = pd.DataFrame(
{
"chr": ["1", "2", "1", "3", "5"],
"start": [10, 100, 6000, 10000, 100000],
"stop": [12, 110, 6110, 11110, 111110],
}
)
df = df.assign(summit=(df["stop"] - df["start"]) / 2)
return df
a = regions.GenomicRegions("shu", sample_data, [], genome)
anno = regions.annotators.NextGenes(still_ok=True)
a.add_annotator(anno)
a.load()
ppg.run_pipegraph()
assert (
a.df["Primary gene stable_id"] == ["fake1", "fake2", "fake3", "", ""]
).all()
should = [
-1.0 * (5000 - (11)),
-1.0 * (6055 - 5400),
-1.0 * (105 - 5400),
numpy.nan,
numpy.nan,
]
assert (
(a.df["Primary gene distance"] == should)
| numpy.isnan(a.df["Primary gene distance"])
).all()
def test_anno_next_genes(self):
genome = DummyGenome(
pd.DataFrame(
[
{
"stable_id": "fake1",
"chr": "1",
"strand": 1,
"tss": 5000,
"tes": 5500,
"description": "bla",
},
{
"stable_id": "fake2",
"chr": "1",
"strand": -1,
"tss": 5400,
"tes": 4900,
"description": "bla",
},
{
"stable_id": "fake3",
"chr": "2",
"strand": -1,
"tss": 5400,
"tes": 4900,
"description": "bla",
},
]
)
)
def sample_data():
df = pd.DataFrame(
{
"chr": ["1", "2", "1", "3", "5"],
"start": [10, 100, 6000, 10000, 100000],
"stop": [12, 110, 6110, 11110, 111110],
}
)
df = df.assign(summit=(df["stop"] - df["start"]) / 2)
return df
a = regions.GenomicRegions("shu", sample_data, [], genome)
anno = regions.annotators.NextGenes(still_ok=True)
a.add_annotator(anno)
a.load()
ppg.run_pipegraph()
assert (a.df["Primary gene stable_id"] == ["fake1", "fake2", "fake3", "", ""]).all()
should = [
-1.0 * (5000 - (11)),
-1.0 * (6055 - 5400),
-1.0 * (105 - 5400),
numpy.nan,
numpy.nan,
]
assert (
(a.df["Primary gene distance"] == should)
| numpy.isnan(a.df["Primary gene distance"])
).all()
| 33.007463
| 92
| 0.310875
| 319
| 4,423
| 4.22884
| 0.278997
| 0.047443
| 0.044477
| 0.062268
| 0.907339
| 0.907339
| 0.907339
| 0.907339
| 0.907339
| 0.907339
| 0
| 0.111906
| 0.55754
| 4,423
| 133
| 93
| 33.255639
| 0.577414
| 0.002713
| 0
| 0.715447
| 0
| 0
| 0.111817
| 0
| 0
| 0
| 0
| 0
| 0.03252
| 1
| 0.03252
| false
| 0
| 0.01626
| 0
| 0.073171
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
6e09f7108b6ce3a479db8ab2bb93200bb1c90ac3
| 6,713
|
py
|
Python
|
executable/demo/scenario_not_obtainables.py
|
westgoten/mechanical-apprentice
|
a4e1bdc9cd62d721bbb9a67c5f94fd83f19fa48d
|
[
"MIT"
] | null | null | null |
executable/demo/scenario_not_obtainables.py
|
westgoten/mechanical-apprentice
|
a4e1bdc9cd62d721bbb9a67c5f94fd83f19fa48d
|
[
"MIT"
] | null | null | null |
executable/demo/scenario_not_obtainables.py
|
westgoten/mechanical-apprentice
|
a4e1bdc9cd62d721bbb9a67c5f94fd83f19fa48d
|
[
"MIT"
] | null | null | null |
import pygame
from constants import *
class LockedDoor(pygame.sprite.Sprite):
def __init__(self, x, y, states_list, key_class, next_s, in_demo):
super().__init__()
self.in_demo = in_demo
self.key_class = key_class
self.next_s = next_s
self.state_index = 0
self.states_list = states_list
self.current_state = self.states_list[self.state_index]
self.image = self.current_state
self.rect = self.image.get_rect()
self.rect.x = x
self.rect.y = y
self.mask = pygame.mask.from_surface(self.image)
def click(self, scenario, inventory):
mouse_pos = pygame.mouse.get_pos()
click_x = mouse_pos[0] - self.rect.x
click_y = mouse_pos[1] - self.rect.y
try:
clicked = self.mask.get_at((click_x, click_y))
except IndexError:
clicked = False
if clicked:
if self.state_index == 0:
#print('A porta está trancada.')
pass
elif self.state_index == 1:
self.state_index += 1
self.current_state = self.states_list[self.state_index]
self.image = self.current_state
self.rect.y = 7
elif self.state_index == 2:
#print('Próximo cenário!')
pass
def item_interaction(self, inventory):
if self.in_demo:
for obj in inventory.objects:
if obj.in_inventory:
if self.rect.collidepoint(obj.rect.center) and isinstance(obj, self.key_class):
self.state_index += 1
self.current_state = self.states_list[self.state_index]
self.image = self.current_state
obj.kill() # Eu deveria colocar in_inventory = False?
class UnlockedDoor(pygame.sprite.Sprite):
def __init__(self, x, y, states_list, next_s, in_demo):
super().__init__()
self.in_demo = in_demo
self.next_s = next_s
self.state_index = 0
self.states_list = states_list
self.current_state = self.states_list[self.state_index]
self.image = self.current_state
self.rect = self.image.get_rect()
self.rect.x = x
self.rect.y = y
self.mask = pygame.mask.from_surface(self.image)
def click(self, scenario, inventory):
mouse_pos = pygame.mouse.get_pos()
click_x = mouse_pos[0] - self.rect.x
click_y = mouse_pos[1] - self.rect.y
try:
clicked = self.mask.get_at((click_x, click_y))
except IndexError:
clicked = False
if clicked:
if self.in_demo:
if self.state_index == 0:
self.state_index += 1
self.current_state = self.states_list[self.state_index]
self.image = self.current_state
elif self.next_s != None:
scenario.next_s = self.next_s
else:
#print('Próximo cenário!')
pass
else:
#print('Área inacessível nesta versão do jogo.')
pass
def item_interaction(self, inventory):
pass
class OpenDoor(pygame.sprite.Sprite):
def __init__(self, x, y, image, next_s, in_demo):
super().__init__()
self.in_demo = in_demo
self.next_s = next_s
self.image = image
self.rect = self.image.get_rect()
self.rect.x = x
self.rect.y = y
self.mask = pygame.mask.from_surface(self.image)
def click(self, scenario, inventory):
mouse_pos = pygame.mouse.get_pos()
click_x = mouse_pos[0] - self.rect.x
click_y = mouse_pos[1] - self.rect.y
try:
clicked = self.mask.get_at((click_x, click_y))
except IndexError:
clicked = False
if clicked:
if self.in_demo:
if self.next_s != None:
scenario.next_s = self.next_s
else:
#print('Próximo cenário!')
pass
else:
#print('Área inacessível nesta versão do jogo.')
pass
def item_interaction(self, inventory):
pass
class Obstacle(pygame.sprite.Sprite):
def __init__(self, start_pos, new_pos, image, obtainable_class, obtainable_args):
super().__init__()
self.obtainable_class = obtainable_class
self.obtainable_args = obtainable_args
self.state = 'IDLE'
self.image = image
self.rect = self.image.get_rect()
self.rect.topleft = start_pos
self.new_pos = new_pos
self.mask = pygame.mask.from_surface(self.image)
def click(self, scenario, inventory):
mouse_pos = pygame.mouse.get_pos()
click_x = mouse_pos[0] - self.rect.x
click_y = mouse_pos[1] - self.rect.y
try:
clicked = self.mask.get_at((click_x, click_y))
except IndexError:
clicked = False
if clicked:
# Lembre-se do efeito sonoro
if self.state == 'IDLE':
self.rect.topleft = self.new_pos
self.state = 'MOVED'
warehousekey_s = self.obtainable_class(*self.obtainable_args)
scenario.obtainables.add(warehousekey_s)
scenario.visible_objects.add(warehousekey_s)
def item_interaction(self, inventory):
pass
class FalseTrail(pygame.sprite.Sprite):
def __init__(self, start_pos, new_pos, image):
super().__init__()
self.state = 'IDLE'
self.image = image
self.rect = self.image.get_rect()
self.rect.topleft = start_pos
self.new_pos = new_pos
self.mask = pygame.mask.from_surface(self.image)
def click(self, scenario, inventory):
mouse_pos = pygame.mouse.get_pos()
click_x = mouse_pos[0] - self.rect.x
click_y = mouse_pos[1] - self.rect.y
try:
clicked = self.mask.get_at((click_x, click_y))
except IndexError:
clicked = False
if clicked:
# Lembre-se do efeito sonoro
if self.state == 'IDLE':
self.rect.topleft = self.new_pos
self.state = 'MOVED'
def item_interaction(self, inventory):
pass
| 29.060606
| 100
| 0.540891
| 793
| 6,713
| 4.336696
| 0.117276
| 0.062809
| 0.056993
| 0.046525
| 0.849666
| 0.819715
| 0.79936
| 0.787729
| 0.778715
| 0.778715
| 0
| 0.004713
| 0.367794
| 6,713
| 230
| 101
| 29.186957
| 0.805608
| 0.043945
| 0
| 0.828025
| 0
| 0
| 0.004208
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.095541
| false
| 0.063694
| 0.012739
| 0
| 0.140127
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
2516a9e7b8a607e2f414e6fd3ad71b4aa6242a70
| 4,013
|
py
|
Python
|
src/v2/permissions.py
|
Diaga/Squibler_Tech_Project
|
df2d91d01ff2e3f4a2d5820e856d9af529ba8eed
|
[
"MIT"
] | null | null | null |
src/v2/permissions.py
|
Diaga/Squibler_Tech_Project
|
df2d91d01ff2e3f4a2d5820e856d9af529ba8eed
|
[
"MIT"
] | null | null | null |
src/v2/permissions.py
|
Diaga/Squibler_Tech_Project
|
df2d91d01ff2e3f4a2d5820e856d9af529ba8eed
|
[
"MIT"
] | null | null | null |
from rest_framework.permissions import BasePermission
from . import models
class IsPOST(BasePermission):
def has_permission(self, request, view):
return request.method == 'POST'
def has_object_permission(self, request, view, obj):
return self.has_permission(request, view)
class IsGET(BasePermission):
def has_permission(self, request, view):
return request.method == 'GET'
def has_object_permission(self, request, view, obj):
return self.has_permission(request, view)
class IsPATCH(BasePermission):
def has_permission(self, request, view):
return request.method == 'PATCH'
def has_object_permission(self, request, view, obj):
return self.has_permission(request, view)
class IsDELETE(BasePermission):
def has_permission(self, request, view):
return request.method == 'DELETE'
def has_object_permission(self, request, view, obj):
return self.has_permission(request, view)
class IsOWNER(BasePermission):
def has_permission(self, request, view):
parent_id = None
if request.method == 'POST':
parent_id = request.data.get('parent',
request.data.get('block', None))
elif request.method == 'GET':
parent_id = request.query_params.get('block', None)
if parent_id is not None:
root = models.TextBlock.objects.find_root(parent_id)[0]
return root.permission_blocks.filter(
user=request.user,
permission=models.PermissionBlock.PermissionEnum.OWNER
).exists()
return True
def has_object_permission(self, request, view, obj):
root = obj if obj.parent is None else \
models.TextBlock.objects.find_root(str(obj.parent.id))[0]
return root.permission_blocks.filter(
user=request.user,
permission=models.PermissionBlock.PermissionEnum.OWNER
).exists()
class IsEDITOR(BasePermission):
def has_permission(self, request, view):
parent_id = None
if request.method == 'POST':
parent_id = request.data.get('parent',
request.data.get('block', None))
elif request.method == 'GET':
parent_id = request.query_params.get('block', None)
if parent_id is not None:
root = models.TextBlock.objects.find_root(parent_id)[0]
return root.permission_blocks.filter(
user=request.user,
permission=models.PermissionBlock.PermissionEnum.EDITOR
).exists()
return True
def has_object_permission(self, request, view, obj):
root = obj if obj.parent is None else \
models.TextBlock.objects.find_root(str(obj.parent.id))[0]
return root.permission_blocks.filter(
user=request.user,
permission=models.PermissionBlock.PermissionEnum.EDITOR
).exists()
class IsVIEW(BasePermission):
def has_permission(self, request, view):
parent_id = None
if request.method == 'POST':
parent_id = request.data.get('parent',
request.data.get('block', None))
elif request.method == 'GET':
parent_id = request.query_params.get('block', None)
if parent_id is not None:
root = models.TextBlock.objects.find_root(parent_id)[0]
return root.permission_blocks.filter(
user=request.user,
permission=models.PermissionBlock.PermissionEnum.VIEW
).exists()
return True
def has_object_permission(self, request, view, obj):
root = obj if obj.parent is None else \
models.TextBlock.objects.find_root(str(obj.parent.id))[0]
return root.permission_blocks.filter(
user=request.user,
permission=models.PermissionBlock.PermissionEnum.VIEW
).exists()
| 32.626016
| 73
| 0.622975
| 447
| 4,013
| 5.467562
| 0.12528
| 0.081015
| 0.120295
| 0.143208
| 0.94108
| 0.94108
| 0.94108
| 0.94108
| 0.94108
| 0.94108
| 0
| 0.002071
| 0.278096
| 4,013
| 122
| 74
| 32.893443
| 0.84156
| 0
| 0
| 0.852273
| 0
| 0
| 0.02168
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.159091
| false
| 0
| 0.022727
| 0.090909
| 0.454545
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
25252a36c7645a014eb1afa1a1462d79b5c2d1b4
| 3,114
|
bzl
|
Python
|
github.com/gogo/protobuf/deps.bzl
|
kalbasit/rules_proto_grpc
|
7e0a97adc8801df1cd74ee435d74bbd857c98a36
|
[
"Apache-2.0"
] | 1
|
2021-08-11T23:14:07.000Z
|
2021-08-11T23:14:07.000Z
|
github.com/gogo/protobuf/deps.bzl
|
kalbasit/rules_proto_grpc
|
7e0a97adc8801df1cd74ee435d74bbd857c98a36
|
[
"Apache-2.0"
] | null | null | null |
github.com/gogo/protobuf/deps.bzl
|
kalbasit/rules_proto_grpc
|
7e0a97adc8801df1cd74ee435d74bbd857c98a36
|
[
"Apache-2.0"
] | null | null | null |
load(":repositories.bzl", "gogo_repos")
# NOTE: THE RULES IN THIS FILE ARE KEPT FOR BACKWARDS COMPATIBILITY ONLY.
# Please use the rules in repositories.bzl
def gogo_proto_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogo_grpc_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogo_proto_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogo_grpc_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogotypes_proto_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogotypes_grpc_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogotypes_proto_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogotypes_grpc_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogoslick_proto_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogoslick_grpc_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogoslick_proto_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogoslick_grpc_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofast_proto_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofast_grpc_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofast_proto_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofast_grpc_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofaster_proto_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofaster_grpc_compile(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofaster_proto_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
def gogofaster_grpc_library(**kwargs):
print("Import of rules in deps.bzl is deprecated, please use repositories.bzl")
gogo_repos(**kwargs)
| 36.635294
| 83
| 0.742132
| 444
| 3,114
| 5.067568
| 0.078829
| 0.146667
| 0.177333
| 0.224
| 0.943556
| 0.943556
| 0.943556
| 0.943556
| 0.943556
| 0.943556
| 0
| 0
| 0.15061
| 3,114
| 84
| 84
| 37.071429
| 0.850662
| 0.037893
| 0
| 0.655738
| 0
| 0
| 0.476779
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.327869
| true
| 0
| 0.327869
| 0
| 0.655738
| 0.327869
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 11
|
2529c16c231ddfe0c7f4b9f69ac4b3db5c4cbeb6
| 6,240
|
py
|
Python
|
day24/monad_unit_test.py
|
pranasziaukas/advent-of-code-2021
|
6b4accf781e48846d154a818891e500cee23e88f
|
[
"MIT"
] | null | null | null |
day24/monad_unit_test.py
|
pranasziaukas/advent-of-code-2021
|
6b4accf781e48846d154a818891e500cee23e88f
|
[
"MIT"
] | null | null | null |
day24/monad_unit_test.py
|
pranasziaukas/advent-of-code-2021
|
6b4accf781e48846d154a818891e500cee23e88f
|
[
"MIT"
] | null | null | null |
import unittest
from monad_unit import Monad
class FooTest(unittest.TestCase):
def setUp(self):
instructions = [
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 1",
"add x 12",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 4",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 1",
"add x 11",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 11",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 1",
"add x 13",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 5",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 1",
"add x 11",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 11",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 1",
"add x 14",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 14",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 26",
"add x -10",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 7",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 1",
"add x 11",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 11",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 26",
"add x -9",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 4",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 26",
"add x -3",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 6",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 1",
"add x 13",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 5",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 26",
"add x -5",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 9",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 26",
"add x -10",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 12",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 26",
"add x -4",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 14",
"mul y x",
"add z y",
"inp w",
"mul x 0",
"add x z",
"mod x 26",
"div z 26",
"add x -5",
"eql x w",
"eql x 0",
"mul y 0",
"add y 25",
"mul y x",
"add y 1",
"mul z y",
"mul y 0",
"add y w",
"add y 14",
"mul y x",
"add z y",
]
self.monad = Monad(instructions)
def test_maximum(self):
self.assertEqual(92915979999498, self.monad.maximize())
def test_minimum(self):
self.assertEqual(21611513911181, self.monad.minimize())
if __name__ == "__main__":
unittest.main()
| 22.857143
| 63
| 0.252244
| 786
| 6,240
| 1.98855
| 0.061069
| 0.143314
| 0.089571
| 0.143314
| 0.822137
| 0.822137
| 0.822137
| 0.822137
| 0.822137
| 0.822137
| 0
| 0.091978
| 0.618429
| 6,240
| 272
| 64
| 22.941176
| 0.564469
| 0
| 0
| 0.916981
| 0
| 0
| 0.288782
| 0
| 0
| 0
| 0
| 0
| 0.007547
| 1
| 0.011321
| false
| 0
| 0.007547
| 0
| 0.022642
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
252b2765662986d8032244d9ebac0905b4424b3a
| 2,342
|
py
|
Python
|
AsianOption.py
|
ydai49/Derivatives
|
081b8e6bc069ec10839c2155f001fe3f14885fdb
|
[
"MIT"
] | null | null | null |
AsianOption.py
|
ydai49/Derivatives
|
081b8e6bc069ec10839c2155f001fe3f14885fdb
|
[
"MIT"
] | null | null | null |
AsianOption.py
|
ydai49/Derivatives
|
081b8e6bc069ec10839c2155f001fe3f14885fdb
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 14:58:36 2020
@author: daiyu
"""
import math
import numpy as np
np.random.seed(1)
class Asian_option:
def __init__(self,S, K, T, r, d,sigma):
self.S, self.K, self.T, self.r, self.d , self.sigma = S, K, T, r, d, sigma
# Monte Carlo Method
def Asian_call(self,n=100000):
np.random.seed(1)
S, K, T, r, d, sigma = self.S, self.K, self.T, self.r, self.d , self.sigma
# delta_T = 1/12
steps = int(T*12)
# set parameters for grid
dt = 1/12
vol = sigma*np.sqrt(dt)
mu = (r-d-0.5*sigma**2)*dt
#generate random walk from lognormal model
random = np.random.lognormal(mean=mu,sigma = vol,size=steps*n).reshape(n,steps)
#Set intial value for simulation path
init = np.ones([n,1])*S
#Simulated path
random_walk = np.concatenate((init,random),axis=1)
price_walk = np.cumprod(random_walk,axis=1)
# Simulated stock price at maturity
Ave_Value_at_M = (price_walk.sum(axis=1)-S)/steps
# option intrinc value
option_value = np.where(Ave_Value_at_M>K,Ave_Value_at_M-K,0)
return option_value.mean()*np.exp(-r*T)
def Asian_put(self,n=100000):
np.random.seed(1)
S, K, T, r, d, sigma = self.S, self.K, self.T, self.r, self.d , self.sigma
# delta_T = 1/12
steps = int(T*12)
# set parameters for grid
dt = 1/12
vol = sigma*np.sqrt(dt)
mu = (r-d-0.5*sigma**2)*dt
#generate random walk from lognormal model
random = np.random.lognormal(mean=mu,sigma = vol,size=steps*n).reshape(n,steps)
#Set intial value for simulation path
init = np.ones([n,1])*S
#Simulated path
random_walk = np.concatenate((init,random),axis=1)
price_walk = np.cumprod(random_walk,axis=1)
# Simulated stock price at maturity
Ave_Value_at_M = (price_walk.sum(axis=1)-S)/steps
# option intrinc value
option_value = np.where(Ave_Value_at_M<K,K-Ave_Value_at_M,0)
return option_value.mean()*np.exp(-r*T)
| 29.64557
| 87
| 0.554227
| 357
| 2,342
| 3.526611
| 0.246499
| 0.009531
| 0.047657
| 0.052423
| 0.867355
| 0.848292
| 0.840349
| 0.840349
| 0.840349
| 0.794281
| 0
| 0.037641
| 0.319385
| 2,342
| 79
| 88
| 29.64557
| 0.752196
| 0.206661
| 0
| 0.735294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.088235
| false
| 0
| 0.058824
| 0
| 0.235294
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
25682dc9d950eeca6c81c9da68588d0d4e71d102
| 47
|
py
|
Python
|
Modulo9/script main.py
|
chemross/practica3
|
ca8ce3eeb49e5e8648d9d10a058b1df56dbde047
|
[
"Apache-2.0"
] | null | null | null |
Modulo9/script main.py
|
chemross/practica3
|
ca8ce3eeb49e5e8648d9d10a058b1df56dbde047
|
[
"Apache-2.0"
] | null | null | null |
Modulo9/script main.py
|
chemross/practica3
|
ca8ce3eeb49e5e8648d9d10a058b1df56dbde047
|
[
"Apache-2.0"
] | null | null | null |
from operacion import random
import random
| 15.666667
| 30
| 0.787234
| 6
| 47
| 6.166667
| 0.666667
| 0.648649
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.212766
| 47
| 2
| 31
| 23.5
| 1
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| 0
| 0
| 0
| 1
| 0
| true
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| 1
| 0
| null | 1
| 0
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| 0
| 0
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| 0
| 0
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| 0
| 1
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
c2d2af1c8a02dbe6cb8a07fee7b72626649f94a9
| 10,623
|
py
|
Python
|
tests/test_schema.py
|
Tolsto/target-bigquery
|
b99d5933ddd259f3ca8f98eb059a99ea1d1f407d
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_schema.py
|
Tolsto/target-bigquery
|
b99d5933ddd259f3ca8f98eb059a99ea1d1f407d
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_schema.py
|
Tolsto/target-bigquery
|
b99d5933ddd259f3ca8f98eb059a99ea1d1f407d
|
[
"BSD-3-Clause"
] | 2
|
2020-10-30T08:38:51.000Z
|
2021-04-20T12:32:09.000Z
|
import singer
from target_bigquery.schema import build_schema
from tests import unittestcore
class TestSimpleStream(unittestcore.BaseUnitTest):
def setUp(self):
super(TestSimpleStream, self).setUp()
def test_flat_schema(self):
schema = '{ "type": "SCHEMA", "stream": "simple_stream", "schema": { "properties": { "id": { "type": [ "null", "string" ] }, "name": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "integer" ] }, "ratio": { "type": [ "null", "number" ] }, "timestamp": { "type": "string", "format": "date-time" }, "date": { "type": "string", "format": "date" } }, "type": [ "null", "object" ] }, "key_properties": [ "id" ], "bookmark_properties": [ "date" ] }'
msg = singer.parse_message(schema)
schema = build_schema(msg.schema, key_properties=msg.key_properties, add_metadata=True)
for f in schema:
if f.name == "id":
self.assertEqual(f.field_type.upper(), "STRING")
elif f.name == "name":
self.assertEqual(f.field_type.upper(), "STRING")
elif f.name == "value":
self.assertEqual(f.field_type.upper(), "INTEGER")
elif f.name == "ratio":
self.assertEqual(f.field_type.upper(), "FLOAT")
elif f.name == "timestamp":
self.assertEqual(f.field_type.upper(), "TIMESTAMP")
elif f.name == "date":
self.assertEqual(f.field_type.upper(), "DATE")
def test_nested_schema(self):
schema = '{ "type": "SCHEMA", "stream": "nested_stream", "schema": { "properties": { "account_id": { "type": [ "null", "string" ] }, "account_name": { "type": [ "null", "string" ] }, "action_values": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "ad_id": { "type": [ "null", "string" ] }, "ad_name": { "type": [ "null", "string" ] }, "adset_id": { "type": [ "null", "string" ] }, "adset_name": { "type": [ "null", "string" ] }, "age": { "type": [ "null", "integer", "string" ] }, "campaign_id": { "type": [ "null", "string" ] }, "campaign_name": { "type": [ "null", "string" ] }, "canvas_avg_view_percent": { "type": [ "null", "number" ] }, "canvas_avg_view_time": { "type": [ "null", "number" ] }, "clicks": { "type": [ "null", "integer" ] }, "conversion_rate_ranking": { "type": [ "null", "string" ] }, "cost_per_action_type": { "items": { "properties": { "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "string" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "cost_per_inline_link_click": { "type": [ "null", "number" ] }, "cost_per_inline_post_engagement": { "type": [ "null", "number" ] }, "cost_per_unique_action_type": { "items": { "properties": { "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "string" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "cost_per_unique_click": { "type": [ "null", "number" ] }, "cost_per_unique_inline_link_click": { "type": [ "null", "number" ] }, "cpc": { "type": [ "null", "number" ] }, "cpm": { "type": [ "null", "number" ] }, "cpp": { "type": [ "null", "number" ] }, "ctr": { "type": [ "null", "number" ] }, "date_start": { "format": "date-time", "type": [ "null", "string" ] }, "date_stop": { "format": "date-time", "type": [ "null", "string" ] }, "engagement_rate_ranking": { "type": [ "null", "string" ] }, "frequency": { "type": [ "null", "number" ] }, "gender": { "type": [ "null", "string" ] }, "impressions": { "type": [ "null", "integer" ] }, "inline_link_click_ctr": { "type": [ "null", "number" ] }, "inline_link_clicks": { "type": [ "null", "integer" ] }, "inline_post_engagement": { "type": [ "null", "integer" ] }, "objective": { "type": [ "null", "string" ] }, "outbound_clicks": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "quality_ranking": { "type": [ "null", "string" ] }, "reach": { "type": [ "null", "integer" ] }, "social_spend": { "type": [ "null", "number" ] }, "spend": { "type": [ "null", "number" ] }, "unique_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "unique_clicks": { "type": [ "null", "integer" ] }, "unique_ctr": { "type": [ "null", "number" ] }, "unique_inline_link_click_ctr": { "type": [ "null", "number" ] }, "unique_inline_link_clicks": { "type": [ "null", "integer" ] }, "unique_link_clicks_ctr": { "type": [ "null", "number" ] }, "video_30_sec_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p100_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p25_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p50_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p75_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_play_curve_actions": { "items": { "properties": { "action_type": { "type": [ "null", "string" ] }, "value": { "items": { "type": [ "null", "integer" ] }, "type": [ "null", "array" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "website_ctr": { "items": { "properties": { "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] } }, "type": [ "null", "object" ] }, "key_properties": [ "campaign_id", "adset_id", "ad_id", "date_start", "age", "gender" ], "bookmark_properties": [ "date_start" ] }'
msg = singer.parse_message(schema)
schema = build_schema(msg.schema, key_properties=msg.key_properties, add_metadata=True)
for f in schema:
if f.name in ("date_start", "date_stop"):
self.assertEqual(f.field_type.upper(), "TIMESTAMP")
# TODO: Actually check nested fields and their data type
| 212.46
| 8,737
| 0.526405
| 1,111
| 10,623
| 4.835284
| 0.10441
| 0.257632
| 0.213701
| 0.106106
| 0.753537
| 0.721705
| 0.634773
| 0.60108
| 0.592889
| 0.592889
| 0
| 0.009506
| 0.178104
| 10,623
| 49
| 8,738
| 216.795918
| 0.605773
| 0.005083
| 0
| 0.333333
| 0
| 0.066667
| 0.876218
| 0.081007
| 0
| 0
| 0
| 0.020408
| 0.233333
| 1
| 0.1
| false
| 0
| 0.1
| 0
| 0.233333
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6c135f6b2473d9b377b6951e159eee293da79be1
| 131
|
py
|
Python
|
virtual_microgrids/agents/__init__.py
|
bmeyers/VirtualMicrogridSegmentation
|
cd9e7ef1a2ccc438a855765e4c07904740ec12ee
|
[
"BSD-2-Clause"
] | 7
|
2020-10-27T21:15:43.000Z
|
2021-12-17T02:57:42.000Z
|
virtual_microgrids/agents/__init__.py
|
bmeyers/VirtualMicrogridSegmentation
|
cd9e7ef1a2ccc438a855765e4c07904740ec12ee
|
[
"BSD-2-Clause"
] | 6
|
2019-03-07T18:11:09.000Z
|
2019-03-13T06:43:12.000Z
|
virtual_microgrids/agents/__init__.py
|
bmeyers/VirtualMicrogridSegmentation
|
cd9e7ef1a2ccc438a855765e4c07904740ec12ee
|
[
"BSD-2-Clause"
] | 2
|
2021-02-02T05:28:05.000Z
|
2021-03-26T03:06:44.000Z
|
from virtual_microgrids.agents.actor_network import ActorNetwork
from virtual_microgrids.agents.critic_network import CriticNetwork
| 65.5
| 66
| 0.916031
| 16
| 131
| 7.25
| 0.625
| 0.189655
| 0.362069
| 0.465517
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.053435
| 131
| 2
| 66
| 65.5
| 0.935484
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
6c397d8b14ddedfdadae76dd04cf345992bf66c3
| 234
|
py
|
Python
|
01_fundamentals/02_operators/bitwise.py
|
doanthanhnhan/learningPY
|
93c10c5225a306c791402095e1cf0b454f31d0c2
|
[
"MIT"
] | 1
|
2021-04-04T02:39:05.000Z
|
2021-04-04T02:39:05.000Z
|
01_fundamentals/02_operators/bitwise.py
|
doanthanhnhan/learningPY
|
93c10c5225a306c791402095e1cf0b454f31d0c2
|
[
"MIT"
] | null | null | null |
01_fundamentals/02_operators/bitwise.py
|
doanthanhnhan/learningPY
|
93c10c5225a306c791402095e1cf0b454f31d0c2
|
[
"MIT"
] | null | null | null |
num1 = 10 # Binary value = 01010
num2 = 20 # Binary Value = 10100
print(num1 & num2) # 00000
print(num1 | num2) # 11110
print(num1 ^ num2) # 11110
print(~num1) # 1111 0101
print(num1 << 3) # 0101 0000
print(num2 >> 3) # 0010
| 23.4
| 33
| 0.628205
| 35
| 234
| 4.2
| 0.457143
| 0.306122
| 0.265306
| 0.244898
| 0.306122
| 0.306122
| 0
| 0
| 0
| 0
| 0
| 0.346369
| 0.235043
| 234
| 9
| 34
| 26
| 0.47486
| 0.358974
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.75
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
6665b1d484de0901f0a3947a12103df1f88c3149
| 2,425
|
py
|
Python
|
foursquare_v1/main/migrations/0004_auto_20191126_1844.py
|
HelloPKJ/foursquare_wx
|
5cfa0e7e5c49c1076d5354e8a734055bafb77970
|
[
"BSD-3-Clause"
] | null | null | null |
foursquare_v1/main/migrations/0004_auto_20191126_1844.py
|
HelloPKJ/foursquare_wx
|
5cfa0e7e5c49c1076d5354e8a734055bafb77970
|
[
"BSD-3-Clause"
] | 6
|
2021-03-19T11:29:08.000Z
|
2022-02-10T12:40:05.000Z
|
foursquare_v1/main/migrations/0004_auto_20191126_1844.py
|
HelloPKJ/foursquare_wx
|
5cfa0e7e5c49c1076d5354e8a734055bafb77970
|
[
"BSD-3-Clause"
] | null | null | null |
# Generated by Django 2.2.7 on 2019-11-26 18:44
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('main', '0003_auto_20191126_1835'),
]
operations = [
migrations.AlterField(
model_name='customertable',
name='customer_data_sources_id',
field=models.PositiveSmallIntegerField(db_index=True, null=True),
),
migrations.AlterField(
model_name='customertable',
name='customer_id',
field=models.AutoField(db_index=True, primary_key=True, serialize=False),
),
migrations.AlterField(
model_name='customertable',
name='customer_region2_cn',
field=models.CharField(db_index=True, max_length=32, null=True),
),
migrations.AlterField(
model_name='customertable',
name='customer_region2_en',
field=models.CharField(db_index=True, max_length=32, null=True),
),
migrations.AlterField(
model_name='customertable',
name='customer_trades_id',
field=models.PositiveSmallIntegerField(db_index=True, null=True),
),
migrations.AlterField(
model_name='datasourcestable',
name='data_sources_id',
field=models.AutoField(db_index=True, primary_key=True, serialize=False),
),
migrations.AlterField(
model_name='datasourcestable',
name='data_sources_name_cn',
field=models.CharField(db_index=True, max_length=32, null=True),
),
migrations.AlterField(
model_name='datasourcestable',
name='data_sources_name_en',
field=models.CharField(db_index=True, max_length=64, null=True),
),
migrations.AlterField(
model_name='tradestable',
name='trades_id',
field=models.AutoField(db_index=True, primary_key=True, serialize=False),
),
migrations.AlterField(
model_name='tradestable',
name='trades_name_cn',
field=models.CharField(db_index=True, max_length=32, null=True),
),
migrations.AlterField(
model_name='tradestable',
name='trades_name_en',
field=models.CharField(db_index=True, max_length=64, null=True),
),
]
| 35.144928
| 85
| 0.602062
| 244
| 2,425
| 5.75
| 0.221311
| 0.156807
| 0.196009
| 0.22737
| 0.890948
| 0.883108
| 0.883108
| 0.844619
| 0.801853
| 0.709907
| 0
| 0.026102
| 0.289072
| 2,425
| 68
| 86
| 35.661765
| 0.787703
| 0.018557
| 0
| 0.709677
| 1
| 0
| 0.149706
| 0.019765
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.016129
| 0
| 0.064516
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
66d31df8b8f1f7e88c31cea183e0be171ab79232
| 550
|
py
|
Python
|
eval_covid20cases_timm-regnetx_002_MedianBlur.py
|
BrunoKrinski/segtool
|
cb604b5f38104c43a76450136e37c3d1c4b6d275
|
[
"MIT"
] | null | null | null |
eval_covid20cases_timm-regnetx_002_MedianBlur.py
|
BrunoKrinski/segtool
|
cb604b5f38104c43a76450136e37c3d1c4b6d275
|
[
"MIT"
] | null | null | null |
eval_covid20cases_timm-regnetx_002_MedianBlur.py
|
BrunoKrinski/segtool
|
cb604b5f38104c43a76450136e37c3d1c4b6d275
|
[
"MIT"
] | null | null | null |
import os
ls=["python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_0_MedianBlur.yml",
"python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_1_MedianBlur.yml",
"python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_2_MedianBlur.yml",
"python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_3_MedianBlur.yml",
"python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_4_MedianBlur.yml",
]
for l in ls:
os.system(l)
| 50
| 104
| 0.849091
| 80
| 550
| 5.4625
| 0.3
| 0.114416
| 0.1373
| 0.217391
| 0.897025
| 0.897025
| 0.897025
| 0.897025
| 0.897025
| 0.897025
| 0
| 0.057803
| 0.056364
| 550
| 11
| 105
| 50
| 0.7842
| 0
| 0
| 0
| 0
| 0
| 0.880218
| 0.653358
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.111111
| 0
| 0.111111
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
dd2127d401495c25443786a6c43f8510f67f38fc
| 168
|
py
|
Python
|
Correlation_Module/spatial_correlation_sampler/__init__.py
|
DenBaum/Pytorch-Correlation-extension
|
328dbf8b2130efb9ffd2674be0799aa63e7c4ed1
|
[
"MIT"
] | null | null | null |
Correlation_Module/spatial_correlation_sampler/__init__.py
|
DenBaum/Pytorch-Correlation-extension
|
328dbf8b2130efb9ffd2674be0799aa63e7c4ed1
|
[
"MIT"
] | null | null | null |
Correlation_Module/spatial_correlation_sampler/__init__.py
|
DenBaum/Pytorch-Correlation-extension
|
328dbf8b2130efb9ffd2674be0799aa63e7c4ed1
|
[
"MIT"
] | null | null | null |
from .spatial_correlation_sampler import SpatialCorrelationSampler, SpatialCorrelationSamplerFunction, SpatialCorrelationSamplerFunctionNew, spatial_correlation_sample
| 84
| 167
| 0.934524
| 11
| 168
| 13.909091
| 0.818182
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.041667
| 168
| 1
| 168
| 168
| 0.950311
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
dd930295716f71bffe5bb0fb358881151d77571f
| 213
|
py
|
Python
|
neural_exploration/visualize/admin.py
|
brookefitzgerald/neural_exploration
|
c820bed5cc800042e40405bf086e6bd4cca0f56f
|
[
"MIT"
] | null | null | null |
neural_exploration/visualize/admin.py
|
brookefitzgerald/neural_exploration
|
c820bed5cc800042e40405bf086e6bd4cca0f56f
|
[
"MIT"
] | null | null | null |
neural_exploration/visualize/admin.py
|
brookefitzgerald/neural_exploration
|
c820bed5cc800042e40405bf086e6bd4cca0f56f
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Experiment, Site, Metadata, BinnedData
admin.site.register(Experiment)
admin.site.register(Site)
admin.site.register(BinnedData)
admin.site.register(Metadata)
| 26.625
| 58
| 0.826291
| 28
| 213
| 6.285714
| 0.392857
| 0.204545
| 0.386364
| 0.306818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075117
| 213
| 7
| 59
| 30.428571
| 0.893401
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
06c959890a7b7c61716427d2ddaf42ebd9776ea3
| 48
|
py
|
Python
|
markov_generator/__init__.py
|
steven-s/markov-text-generator
|
d7c1b92e3bd31dbcdedbd6f068f2dbed43df6328
|
[
"MIT"
] | null | null | null |
markov_generator/__init__.py
|
steven-s/markov-text-generator
|
d7c1b92e3bd31dbcdedbd6f068f2dbed43df6328
|
[
"MIT"
] | null | null | null |
markov_generator/__init__.py
|
steven-s/markov-text-generator
|
d7c1b92e3bd31dbcdedbd6f068f2dbed43df6328
|
[
"MIT"
] | null | null | null |
from markov_generator.markov_generator import *
| 24
| 47
| 0.875
| 6
| 48
| 6.666667
| 0.666667
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 48
| 1
| 48
| 48
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
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