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3cdac67519b7d839a35d51f0c198d181563deb1c
snsokolov/contests
codeforces/580C_park.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 0 # DFS to determine parent-child d = collections.deque([self.nodes[0]]) visited = set() while d: v = d.pop() visited.add(v.i) for e in v.edges: ...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 0 d = collections.deque([self.nodes[0]]) visited = set() while d: v = d.pop() visited.add(v.i) for e in v.edges: if e.i not in visited: v.add_child(e) d.append(e) ...
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Main calcualtion function of the class
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b09b4a5a818d9ea59be30e79ef2e11877905f0ad
snsokolov/contests
codeforces/560A_currency.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 1 for n in self.nums: if n == 1: result = -1 break return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
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def calculate(self): result = 1 for n in self.nums: if n == 1: result = -1 break return str(result)
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Main calcualtion function of the class
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9e2188e8db9143b40c93634c6f6e07a3e2b2c83c
snsokolov/contests
codeforces/554A_photo.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ s = set() # Brute force loop for i in range(len(self.str) + 1): for c in list(self.LC): s.add(self.insert(i, c)) return len(s)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): s = set() for i in range(len(self.str) + 1): for c in list(self.LC): s.add(self.insert(i, c)) return len(s)
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Main calcualtion function of the class
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5b410c9a6285c8e56f52e9ea5610fa05a670f43a
snsokolov/contests
topcoder/673A_BearSong.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = self.countRareNotes(self.test_inputs) return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
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def calculate(self): result = self.countRareNotes(self.test_inputs) return str(result)
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Main calcualtion function of the class
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64d505760cb50b09bfed6316b306ed808c5af240
snsokolov/contests
codeforces/554B_clean.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 0 d = {} for i in range(len(self.list)): key = self.r2dec(i) if key in d: d[key] += 1 else: d[key] = 1 for n in d: if resu...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 0 d = {} for i in range(len(self.list)): key = self.r2dec(i) if key in d: d[key] += 1 else: d[key] = 1 for n in d: if result < d[n]: result = d[n] return ...
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Main calcualtion function of the class
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e6cce2e179a41cf42afcec9a4137fbe78dcbd3e4
snsokolov/contests
codeforces/557B_tea.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = min(self.wmax, self.w) return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
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def calculate(self): result = min(self.wmax, self.w) return str(result)
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Main calcualtion function of the class
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3a3240f8d082ee88ea0eabdb3837b564a5332b11
snsokolov/contests
codeforces/556A_zeroes.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 0 for n in self.list: result += 1 if n else -1 return str(abs(result))
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 0 for n in self.list: result += 1 if n else -1 return str(abs(result))
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Main calcualtion function of the class
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97186ece1fab8c9a3a1233f8430edd01f5f9a9b6
snsokolov/contests
codeforces/572A_arrays.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = "YES" if self.na[self.k-1] < self.nb[-self.m] else "NO" return result
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = "YES" if self.na[self.k-1] < self.nb[-self.m] else "NO" return result
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Main calcualtion function of the class
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fc5959d7bd38cae5280d0071e3b1fa6c2f659745
snsokolov/contests
codeforces/672B_good.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ freq = [0]*26 for c in self.s: ltr = ord(c) - ord('a') freq[ltr] += 1 zeroes = 0 extra = 0 for n in freq: if n == 0: zeroes += 1 if n > 1:...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): freq = [0]*26 for c in self.s: ltr = ord(c) - ord('a') freq[ltr] += 1 zeroes = 0 extra = 0 for n in freq: if n == 0: zeroes += 1 if n > 1: extra += n - 1 result = -1 if ex...
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Main calcualtion function of the class
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[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
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1e90cf03c492badfa1988a391a9bdbe57227576b
snsokolov/contests
codeforces/580B_company.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 0 # Sorting both arrays by using first array as a key self.numa, self.numb = zip(*sorted(zip(self.numa, self.numb))) # Calculating partial sums self.psum = [0] for i in range(self.n): ...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 0 self.numa, self.numb = zip(*sorted(zip(self.numa, self.numb))) self.psum = [0] for i in range(self.n): self.psum.append(self.psum[-1] + self.numb[i]) for i in range(self.n): mrb = self.numa[i] + self.d sum = self...
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Main calcualtion function of the class
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[ "\"\"\" Main calcualtion function of the class \"\"\"", "# Sorting both arrays by using first array as a key", "# Calculating partial sums", "# Calculating result" ]
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3ac69c12801acb3c7535e360136c0b4268398f0d
snsokolov/contests
codeforces/793B_way.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = "NO" for x in self.linkedx: if self.is_linked((x, self.start[1]), (x, self.end[1])): result = "YES" break for y in self.linkedy: if self.is_linked((self....
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = "NO" for x in self.linkedx: if self.is_linked((x, self.start[1]), (x, self.end[1])): result = "YES" break for y in self.linkedy: if self.is_linked((self.start[0], y), (self.end[0], y)): result =...
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Main calcualtion function of the class
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[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
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c3731b7b9b613813b7408647c35a17ff8658fad7
snsokolov/contests
codeforces/669B_gh.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = "FINITE" pos = 0 vis = set([]) while 0 <= pos < self.n: vis.add(pos) if self.numa[pos]: pos += self.numb[pos] else: pos -= self.numb[p...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = "FINITE" pos = 0 vis = set([]) while 0 <= pos < self.n: vis.add(pos) if self.numa[pos]: pos += self.numb[pos] else: pos -= self.numb[pos] if pos in vis: result = ...
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Main calcualtion function of the class
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[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
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5f7f256bc6941c7a1c5264b4cf626e0b90eb2e09
snsokolov/contests
codeforces/669C_matrix.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ matrix = [] for r in range(self.n): row = [0] * self.m matrix.append(row) for cmd in reversed(self.nums): if cmd[0] == 3: matrix[cmd[1]-1][cmd[2]-1] = cmd[3] ...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): matrix = [] for r in range(self.n): row = [0] * self.m matrix.append(row) for cmd in reversed(self.nums): if cmd[0] == 3: matrix[cmd[1]-1][cmd[2]-1] = cmd[3] elif cmd[0] == 1: rep = matrix[cmd[1]...
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Main calcualtion function of the class
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[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
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40b9e0e328c650bfbf3c2e6d3ba0d8d5b8c6edcc
snsokolov/contests
codeforces/621C_flowers.py
[ "Unlicense" ]
Python
prob
<not_specific>
def prob(self, l, r, p): """ Probability of a number in range divided by prime """ w = r - l + 1 lm = l % p rm = r % p n = (r - rm - (l - lm)) // p if lm == 0: n += 1 return n / w
Probability of a number in range divided by prime
Probability of a number in range divided by prime
[ "Probability", "of", "a", "number", "in", "range", "divided", "by", "prime" ]
def prob(self, l, r, p): w = r - l + 1 lm = l % p rm = r % p n = (r - rm - (l - lm)) // p if lm == 0: n += 1 return n / w
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Probability of a number in range divided by prime
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[ "\"\"\" Probability of a number in range divided by prime \"\"\"" ]
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40b9e0e328c650bfbf3c2e6d3ba0d8d5b8c6edcc
snsokolov/contests
codeforces/621C_flowers.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ self.pr = [] for i in range(self.n): self.pr.append(1 - self.prob(self.numa[i], self.numb[i], self.p)) result = 0 prev = self.pr[-1] for p in self.pr: result += (1 - p*prev) ...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): self.pr = [] for i in range(self.n): self.pr.append(1 - self.prob(self.numa[i], self.numb[i], self.p)) result = 0 prev = self.pr[-1] for p in self.pr: result += (1 - p*prev) prev = p return str(result*2000)
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Main calcualtion function of the class
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[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
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fc01bf0db2be446fac49c6d48007740e20a1dfe8
snsokolov/contests
codeforces/556D_fug.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ for it in self.iterate(): pass for n in self.result: if n is None: return "No" answer = "Yes\n" answer += " ".join(self.result) return answer
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): for it in self.iterate(): pass for n in self.result: if n is None: return "No" answer = "Yes\n" answer += " ".join(self.result) return answer
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Main calcualtion function of the class
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4a0895349baa18b3b704b859e66c9758cdd55b1b
snsokolov/contests
codeforces/556D_fug_fastlist2.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ for i in range(self.gn): g = self.gsrt[i] it = self.a.lower_bound((g[1], 0)) if not it.iter_end(): alb = it.iter_getitem() if alb[0] > g[0]: return...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): for i in range(self.gn): g = self.gsrt[i] it = self.a.lower_bound((g[1], 0)) if not it.iter_end(): alb = it.iter_getitem() if alb[0] > g[0]: return "No" self.result[g[2]] = alb[1]+1 ...
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
2649500321d70249bdf3abc259d6ed29ac6af826
snsokolov/contests
codeforces/667A_rain.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = "NO" pi = 3.141592653589793238462 vv = float(self.v) * 4 / (pi * self.d * self.d) if vv > self.e: result = "YES\n" result += str(float(self.h) / (vv - self.e)) return st...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = "NO" pi = 3.141592653589793238462 vv = float(self.v) * 4 / (pi * self.d * self.d) if vv > self.e: result = "YES\n" result += str(float(self.h) / (vv - self.e)) return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
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78a949be06363f83b06dc0f57b33b9a0ed68efc6
snsokolov/contests
codeforces/609A_first.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 0 sum = 0 for i in range(self.n): sum += self.numss[i] result += 1 if (sum >= self.m): break return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 0 sum = 0 for i in range(self.n): sum += self.numss[i] result += 1 if (sum >= self.m): break return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
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4687f94a820fa6ab3986b277062beba9aee5ef4d
snsokolov/contests
codeforces/580A_steps.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = m = 1 for i in range(len(self.nums)): if i != 0: m = m + 1 if self.nums[i] >= self.nums[i-1] else 1 result = max(result, m) return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = m = 1 for i in range(len(self.nums)): if i != 0: m = m + 1 if self.nums[i] >= self.nums[i-1] else 1 result = max(result, m) return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
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a887ad7073a23b17c267a73db45ff3b9d984ff16
snsokolov/contests
codeforces/577C_game.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = [] for (i, k) in enumerate(self.pf): if k != 0 or i < 2: continue cur = i while cur <= self.n: result.append(cur) cur *= i re...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = [] for (i, k) in enumerate(self.pf): if k != 0 or i < 2: continue cur = i while cur <= self.n: result.append(cur) cur *= i return str(len(result)) + "\n" + " ".join(map(str, result))
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Main calcualtion function of the class
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[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
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24eadedfbc36fe622fabdb53d895d0e46d63e8e2
snsokolov/contests
codeforces/560C_hexagon.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = self.low + self.hi return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = self.low + self.hi return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
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674607ec72449dd5353677b7085c674e60ae69ad
snsokolov/contests
codeforces/560B_art.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ for rec in self.remain: if self.rmin[0] <= rec[0] and self.rmin[1] <= rec[1]: return "YES" if self.rmin[1] <= rec[0] and self.rmin[0] <= rec[1]: return "YES" return "NO"
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): for rec in self.remain: if self.rmin[0] <= rec[0] and self.rmin[1] <= rec[1]: return "YES" if self.rmin[1] <= rec[0] and self.rmin[0] <= rec[1]: return "YES" return "NO"
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
e35857fc032e2f24ae019109f29fc1663b6bdf91
snsokolov/contests
codeforces/552A_table.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ sum = 0 for y in range(1, self.N+2): for x in range(1, self.N+2): for (start, end) in zip(self.starts, self.ends): inside = ( x >= start[0] and y >= start[1...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): sum = 0 for y in range(1, self.N+2): for x in range(1, self.N+2): for (start, end) in zip(self.starts, self.ends): inside = ( x >= start[0] and y >= start[1] and x <= end[0] and y <= end[...
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
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e35857fc032e2f24ae019109f29fc1663b6bdf91
snsokolov/contests
codeforces/552A_table.py
[ "Unlicense" ]
Python
decode_inputs
<not_specific>
def decode_inputs(inputs): """ Decoding input string list into base class args list """ num = int(inputs[0]) # Decoding input into a list of integers ilist = [[int(n) for n in i.split()] for i in inputs[1:]] starts = [(rec[0], rec[1]) for rec in ilist] ends = [(rec[2], rec[3]) for rec in ilist...
Decoding input string list into base class args list
Decoding input string list into base class args list
[ "Decoding", "input", "string", "list", "into", "base", "class", "args", "list" ]
def decode_inputs(inputs): num = int(inputs[0]) ilist = [[int(n) for n in i.split()] for i in inputs[1:]] starts = [(rec[0], rec[1]) for rec in ilist] ends = [(rec[2], rec[3]) for rec in ilist] args = [num, starts, ends] return args
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Decoding input string list into base class args list
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[ "\"\"\" Decoding input string list into base class args list \"\"\"", "# Decoding input into a list of integers" ]
[ { "param": "inputs", "type": null } ]
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e35857fc032e2f24ae019109f29fc1663b6bdf91
snsokolov/contests
codeforces/552A_table.py
[ "Unlicense" ]
Python
main
null
def main(): """ Main function. Not called by unit tests """ # Read test input string list n = input() squares = [input() for i in range(int(n))] inputs = [n, squares] # Print the result print(calculate(inputs))
Main function. Not called by unit tests
Main function. Not called by unit tests
[ "Main", "function", ".", "Not", "called", "by", "unit", "tests" ]
def main(): n = input() squares = [input() for i in range(int(n))] inputs = [n, squares] print(calculate(inputs))
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Main function.
[ "Main", "function", "." ]
[ "\"\"\" Main function. Not called by unit tests \"\"\"", "# Read test input string list", "# Print the result" ]
[]
{ "returns": [], "raises": [], "params": [], "outlier_params": [], "others": [] }
d3d6f827054edd0077bcd93f5c8c8becd3214448
snsokolov/contests
codeforces/560D_strings.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = self.splitcheck((0, len(self.stra)), (0, len(self.strb))) return str("YES" if result else "NO")
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = self.splitcheck((0, len(self.stra)), (0, len(self.strb))) return str("YES" if result else "NO")
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
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d3d6f827054edd0077bcd93f5c8c8becd3214448
snsokolov/contests
codeforces/560D_strings.py
[ "Unlicense" ]
Python
uinput
<not_specific>
def uinput(): """ Unit-testable input function wrapper """ if it: return next(it) else: return sys.stdin.readline().rstrip()
Unit-testable input function wrapper
Unit-testable input function wrapper
[ "Unit", "-", "testable", "input", "function", "wrapper" ]
def uinput(): if it: return next(it) else: return sys.stdin.readline().rstrip()
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Unit-testable input function wrapper
[ "Unit", "-", "testable", "input", "function", "wrapper" ]
[ "\"\"\" Unit-testable input function wrapper \"\"\"" ]
[]
{ "returns": [], "raises": [], "params": [], "outlier_params": [], "others": [] }
5d0d9641f364c1c6724a204c74adc478b0b4a64f
snsokolov/contests
codeforces/604C_thinking.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 1 empty = 0 prev = self.s[0] for i in range(1, self.n): cur = self.s[i] if cur != prev: result += 1 else: empty += 1 prev ...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 1 empty = 0 prev = self.s[0] for i in range(1, self.n): cur = self.s[i] if cur != prev: result += 1 else: empty += 1 prev = cur result += min(empty, 2) return str...
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
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ad503153222bba6407e4f744b4193d5104dfacab
snsokolov/contests
codeforces/579A_bacteria.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 1 cur = self.n while cur != 1: if cur % 2: result += 1 cur -= 1 while not cur % 2: cur //= 2 return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 1 cur = self.n while cur != 1: if cur % 2: result += 1 cur -= 1 while not cur % 2: cur //= 2 return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
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3937813a6a65f1f832217d1c4000b527360a3e21
snsokolov/contests
codeforces/560E_chess.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = self.ways[-1] return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = self.ways[-1] return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
6edb0c146b095f0f839362c1381eac931bb55cfa
snsokolov/contests
codeforces/558A_apple.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = max(self.tot1, self.tot2) return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = max(self.tot1, self.tot2) return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
6079c9a4243c2076c289c525c61fb3746bdef97b
snsokolov/contests
codeforces/556D_fug_fastlist.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ for it in self.iterate(): pass for n in self.result: if n is None: return "No" answer = "Yes\n" + " ".join(str(n) for n in self.result) return answer
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): for it in self.iterate(): pass for n in self.result: if n is None: return "No" answer = "Yes\n" + " ".join(str(n) for n in self.result) return answer
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
4f677f2cdca9dc611bbb30f57e6677b4cf2340f3
snsokolov/contests
codeforces/596B_array.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 0 delta = 0 for i in range(self.n): result += abs(self.nums[i] - delta) delta = self.nums[i] return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 0 delta = 0 for i in range(self.n): result += abs(self.nums[i] - delta) delta = self.nums[i] return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
49a06d7083f2c59cfe5577ff10facb824840a49e
snsokolov/contests
codeforces/621B_bishops.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = self.pairs return str(result)
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = self.pairs return str(result)
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
c50cb3afd11bba3e7e7119599bf6705a8ad0436e
snsokolov/contests
codeforces/552C_scales.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ while self.step(): pass return "YES" if self.yes else "NO"
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): while self.step(): pass return "YES" if self.yes else "NO"
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
c50cb3afd11bba3e7e7119599bf6705a8ad0436e
snsokolov/contests
codeforces/552C_scales.py
[ "Unlicense" ]
Python
decode_inputs
<not_specific>
def decode_inputs(inputs): """ Decoding input string list into base class args list """ # Decoding input into a list of integers ilist = [int(i) for i in inputs[0].split()] return ilist
Decoding input string list into base class args list
Decoding input string list into base class args list
[ "Decoding", "input", "string", "list", "into", "base", "class", "args", "list" ]
def decode_inputs(inputs): ilist = [int(i) for i in inputs[0].split()] return ilist
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Decoding input string list into base class args list
[ "Decoding", "input", "string", "list", "into", "base", "class", "args", "list" ]
[ "\"\"\" Decoding input string list into base class args list \"\"\"", "# Decoding input into a list of integers" ]
[ { "param": "inputs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "inputs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
c50cb3afd11bba3e7e7119599bf6705a8ad0436e
snsokolov/contests
codeforces/552C_scales.py
[ "Unlicense" ]
Python
main
null
def main(): """ Main function. Not called by unit tests """ # Read test input string list inputs = [input()] # Print the result print(calculate(inputs))
Main function. Not called by unit tests
Main function. Not called by unit tests
[ "Main", "function", ".", "Not", "called", "by", "unit", "tests" ]
def main(): inputs = [input()] print(calculate(inputs))
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Main function.
[ "Main", "function", "." ]
[ "\"\"\" Main function. Not called by unit tests \"\"\"", "# Read test input string list", "# Print the result" ]
[]
{ "returns": [], "raises": [], "params": [], "outlier_params": [], "others": [] }
9097826791ba50d2beefd076f3afeba76e780fa6
snsokolov/contests
codeforces/556B_fake.py
[ "Unlicense" ]
Python
calculate
<not_specific>
def calculate(self): """ Main calcualtion function of the class """ result = 0 for i in range(self.n): prev = -1 match = 1 for n in self.list: if n == prev + 1: pass else: match = 0 ...
Main calcualtion function of the class
Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
def calculate(self): result = 0 for i in range(self.n): prev = -1 match = 1 for n in self.list: if n == prev + 1: pass else: match = 0 prev = n if match: ...
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Main calcualtion function of the class
[ "Main", "calcualtion", "function", "of", "the", "class" ]
[ "\"\"\" Main calcualtion function of the class \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
a561d81b9acb7c0207dad77e0c222065aeb5c9ef
dj-application/interactive-jupyter
voila/voila/handler.py
[ "BSD-3-Clause" ]
Python
fix_notebook
null
def fix_notebook(self, notebook): """Returns a notebook object with a valid kernelspec. In case the kernel is not found, we search for a matching kernel based on the language. """ # Fetch kernel name from the notebook metadata if 'kernelspec' not in notebook.metadata: ...
Returns a notebook object with a valid kernelspec. In case the kernel is not found, we search for a matching kernel based on the language.
Returns a notebook object with a valid kernelspec. In case the kernel is not found, we search for a matching kernel based on the language.
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def fix_notebook(self, notebook): if 'kernelspec' not in notebook.metadata: notebook.metadata.kernelspec = nbformat.NotebookNode() kernelspec = notebook.metadata.kernelspec kernel_name = kernelspec.get('name', self.kernel_manager.default_kernel_name) all_kernel_specs = yield ...
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Returns a notebook object with a valid kernelspec.
[ "Returns", "a", "notebook", "object", "with", "a", "valid", "kernelspec", "." ]
[ "\"\"\"Returns a notebook object with a valid kernelspec.\n\n In case the kernel is not found, we search for a matching kernel based on the language.\n \"\"\"", "# Fetch kernel name from the notebook metadata", "# We use `maybe_future` to support RemoteKernelSpecManager", "# Find a spec matching...
[ { "param": "self", "type": null }, { "param": "notebook", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "notebook", "type": null, "docstring": null, "docstring_tokens...
a561d81b9acb7c0207dad77e0c222065aeb5c9ef
dj-application/interactive-jupyter
voila/voila/handler.py
[ "BSD-3-Clause" ]
Python
find_kernel_name_for_language
null
def find_kernel_name_for_language(self, kernel_language, kernel_specs=None): """Finds a best matching kernel name given a kernel language. If multiple kernels matches are found, we try to return the same kernel name each time. """ if kernel_specs is None: kernel_specs = yiel...
Finds a best matching kernel name given a kernel language. If multiple kernels matches are found, we try to return the same kernel name each time.
Finds a best matching kernel name given a kernel language. If multiple kernels matches are found, we try to return the same kernel name each time.
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def find_kernel_name_for_language(self, kernel_language, kernel_specs=None): if kernel_specs is None: kernel_specs = yield tornado.gen.maybe_future(self.kernel_spec_manager.get_all_specs()) matches = [ name for name, kernel in kernel_specs.items() if kernel["spec"]["l...
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Finds a best matching kernel name given a kernel language.
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[ "\"\"\"Finds a best matching kernel name given a kernel language.\n\n If multiple kernels matches are found, we try to return the same kernel name each time.\n \"\"\"", "# Sort by display name to get the same kernel each time.", "# TODO py2: replace by return" ]
[ { "param": "self", "type": null }, { "param": "kernel_language", "type": null }, { "param": "kernel_specs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "kernel_language", "type": null, "docstring": null, "docstring...
ba3bfb5477e4b256f367f9e73884f54ff5623503
bitranox/lib_travis
lib_travis/lib_travis_cli.py
[ "MIT" ]
Python
cli_run
None
def cli_run( description: str, command: str, retry: int, sleep: int, banner: bool ) -> None: """ run string command wrapped in run/success/error banners """ lib_travis.run(description, command, retry=retry, sleep=sleep, banner=banner)
run string command wrapped in run/success/error banners
run string command wrapped in run/success/error banners
[ "run", "string", "command", "wrapped", "in", "run", "/", "success", "/", "error", "banners" ]
def cli_run( description: str, command: str, retry: int, sleep: int, banner: bool ) -> None: lib_travis.run(description, command, retry=retry, sleep=sleep, banner=banner)
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run string command wrapped in run/success/error banners
[ "run", "string", "command", "wrapped", "in", "run", "/", "success", "/", "error", "banners" ]
[ "\"\"\" run string command wrapped in run/success/error banners \"\"\"" ]
[ { "param": "description", "type": "str" }, { "param": "command", "type": "str" }, { "param": "retry", "type": "int" }, { "param": "sleep", "type": "int" }, { "param": "banner", "type": "bool" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "description", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "command", "type": "str", "docstring": null, "docstrin...
0c3cbc8ebf83a469a3e2469431151685023cd4ad
proboscis/omni-converter
omni_converter/coconut/auto_data.py
[ "MIT" ]
Python
create_cast_rule
<not_specific>
def create_cast_rule(rule, name=None, _swap=False, cost=1): # def create_cast_rule(rule,name=None,_swap=False,cost=1): """ rule: State->List[State] # should return list of possible casts without data conversion. """ # """ return (_CastLambda(rule, name, _swap, cost=cost))
rule: State->List[State] # should return list of possible casts without data conversion.
State->List[State] # should return list of possible casts without data conversion.
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def create_cast_rule(rule, name=None, _swap=False, cost=1): return (_CastLambda(rule, name, _swap, cost=cost))
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rule: State->List[State] # should return list of possible casts without data conversion.
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[ "# def create_cast_rule(rule,name=None,_swap=False,cost=1):", "\"\"\"\n rule: State->List[State] # should return list of possible casts without data conversion.\n \"\"\"", "# \"\"\"" ]
[ { "param": "rule", "type": null }, { "param": "name", "type": null }, { "param": "_swap", "type": null }, { "param": "cost", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "rule", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "name", "type": null, "docstring": null, "docstring_tokens": [...
0c3cbc8ebf83a469a3e2469431151685023cd4ad
proboscis/omni-converter
omni_converter/coconut/auto_data.py
[ "MIT" ]
Python
add_cast
null
def add_cast(self, rule, name=None): # def add_cast(self,rule,name=None): """ rule: State->List[State] # should return list of possible casts without data conversion. """ # """ self.add_conversion(AutoSolver.create_cast_rule(rule, name=name, _swap=True))
rule: State->List[State] # should return list of possible casts without data conversion.
State->List[State] # should return list of possible casts without data conversion.
[ "State", "-", ">", "List", "[", "State", "]", "#", "should", "return", "list", "of", "possible", "casts", "without", "data", "conversion", "." ]
def add_cast(self, rule, name=None): self.add_conversion(AutoSolver.create_cast_rule(rule, name=name, _swap=True))
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rule: State->List[State] # should return list of possible casts without data conversion.
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[ "# def add_cast(self,rule,name=None):", "\"\"\"\n rule: State->List[State] # should return list of possible casts without data conversion.\n \"\"\"", "# \"\"\"" ]
[ { "param": "self", "type": null }, { "param": "rule", "type": null }, { "param": "name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "rule", "type": null, "docstring": null, "docstring_tokens": [...
b9433b1eeeb5c1c4b3ac115ac70fc3cf0a9690f6
rainforestapp/destimator
destimator/described_estimator.py
[ "MIT" ]
Python
from_file
<not_specific>
def from_file(cls, f): """ Read the described classifier from file. `f` can be a path or a file-like object. """ zf = zipfile.ZipFile(f) extract_dir = tempfile.mkdtemp() try: zf.extractall(extract_dir) data = {} for fn in zf.na...
Read the described classifier from file. `f` can be a path or a file-like object.
Read the described classifier from file. `f` can be a path or a file-like object.
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def from_file(cls, f): zf = zipfile.ZipFile(f) extract_dir = tempfile.mkdtemp() try: zf.extractall(extract_dir) data = {} for fn in zf.namelist(): fn_full = os.path.join(extract_dir, fn) if fn == 'model.bin': ...
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Read the described classifier from file.
[ "Read", "the", "described", "classifier", "from", "file", "." ]
[ "\"\"\"\n Read the described classifier from file. `f` can be a path or a\n file-like object.\n\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "f", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "f", "type": null, "docstring": null, "docstring_tokens": [], ...
b9433b1eeeb5c1c4b3ac115ac70fc3cf0a9690f6
rainforestapp/destimator
destimator/described_estimator.py
[ "MIT" ]
Python
from_url
<not_specific>
def from_url(cls, url): """Read the described classifier from the given URL.""" f = StringIO() f.write(requests.get(url, stream=True).content) return cls.from_file(f)
Read the described classifier from the given URL.
Read the described classifier from the given URL.
[ "Read", "the", "described", "classifier", "from", "the", "given", "URL", "." ]
def from_url(cls, url): f = StringIO() f.write(requests.get(url, stream=True).content) return cls.from_file(f)
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Read the described classifier from the given URL.
[ "Read", "the", "described", "classifier", "from", "the", "given", "URL", "." ]
[ "\"\"\"Read the described classifier from the given URL.\"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "url", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "url", "type": null, "docstring": null, "docstring_tokens": [],...
b9433b1eeeb5c1c4b3ac115ac70fc3cf0a9690f6
rainforestapp/destimator
destimator/described_estimator.py
[ "MIT" ]
Python
is_compatible
<not_specific>
def is_compatible(self, other): """ Check whether this estimator is compatible with the other one (in other words, are their `feature_names` the same). """ return self.feature_names == other.feature_names
Check whether this estimator is compatible with the other one (in other words, are their `feature_names` the same).
Check whether this estimator is compatible with the other one (in other words, are their `feature_names` the same).
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def is_compatible(self, other): return self.feature_names == other.feature_names
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Check whether this estimator is compatible with the other one (in other words, are their `feature_names` the same).
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[ "\"\"\"\n Check whether this estimator is compatible with the other one (in other\n words, are their `feature_names` the same).\n\n \"\"\"" ]
[ { "param": "self", "type": null }, { "param": "other", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "other", "type": null, "docstring": null, "docstring_tokens": ...
0c1574c7c5866ce02c812fc1be43d2e1812a3deb
cloudedbats/cloudedbats_dsp
dsp4bats/wave_file_utils.py
[ "MIT" ]
Python
extract_metadata
<not_specific>
def extract_metadata(self, filepath): """ Used to extract file name parts from sound files created by CloudedBats-WURB. Format: <recorder-id>_<time>_<position>_<rec-type>_<comments>.wav Example: wurb1_20170611T005215+0200_N57.6548E12.6711_TE384_Mdau-in-tandem.wav """ meta...
Used to extract file name parts from sound files created by CloudedBats-WURB. Format: <recorder-id>_<time>_<position>_<rec-type>_<comments>.wav Example: wurb1_20170611T005215+0200_N57.6548E12.6711_TE384_Mdau-in-tandem.wav
Used to extract file name parts from sound files created by CloudedBats-WURB.
[ "Used", "to", "extract", "file", "name", "parts", "from", "sound", "files", "created", "by", "CloudedBats", "-", "WURB", "." ]
def extract_metadata(self, filepath): meta_dict = {} path = pathlib.Path(filepath) meta_dict['file_path'] = str(filepath) meta_dict['abs_file_path'] = str(path.absolute().resolve()) meta_dict['dir_path'] = str(path.parent) meta_dict['file_name'] = path.name meta_d...
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Used to extract file name parts from sound files created by CloudedBats-WURB.
[ "Used", "to", "extract", "file", "name", "parts", "from", "sound", "files", "created", "by", "CloudedBats", "-", "WURB", "." ]
[ "\"\"\" Used to extract file name parts from sound files created by CloudedBats-WURB.\n Format: <recorder-id>_<time>_<position>_<rec-type>_<comments>.wav\n Example: wurb1_20170611T005215+0200_N57.6548E12.6711_TE384_Mdau-in-tandem.wav\n \"\"\"", "# File and dir info.", "# Extract par...
[ { "param": "self", "type": null }, { "param": "filepath", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "filepath", "type": null, "docstring": null, "docstring_tokens...
c7839187c5ffe19f56407232afae0b150cf83fce
rienafairefr/procedural-bricks
proceduralbricks/fill_area.py
[ "MIT" ]
Python
quantizetopalette
<not_specific>
def quantizetopalette(silf, palette, dither=False): """Convert an RGB or L mode image to use a given P image's palette.""" silf.load() # use palette from reference image palette.load() if palette.mode != "P": raise ValueError("bad mode for palette image") if silf.mode != "RGB" and silf...
Convert an RGB or L mode image to use a given P image's palette.
Convert an RGB or L mode image to use a given P image's palette.
[ "Convert", "an", "RGB", "or", "L", "mode", "image", "to", "use", "a", "given", "P", "image", "'", "s", "palette", "." ]
def quantizetopalette(silf, palette, dither=False): silf.load() palette.load() if palette.mode != "P": raise ValueError("bad mode for palette image") if silf.mode != "RGB" and silf.mode != "L": raise ValueError( "only RGB or L mode images can be quantized to a palette" ...
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Convert an RGB or L mode image to use a given P image's palette.
[ "Convert", "an", "RGB", "or", "L", "mode", "image", "to", "use", "a", "given", "P", "image", "'", "s", "palette", "." ]
[ "\"\"\"Convert an RGB or L mode image to use a given P image's palette.\"\"\"", "# use palette from reference image", "# the 0 above means turn OFF dithering", "# Later versions of Pillow (4.x) rename _makeself to _new" ]
[ { "param": "silf", "type": null }, { "param": "palette", "type": null }, { "param": "dither", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "silf", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "palette", "type": null, "docstring": null, "docstring_tokens"...
4ed5ae66c19b92e6cd5fb7d7d3a9a194a91d0f64
coderzbx/seg-mxnet
nnvm/tvm/topi/python/topi/cuda/pooling.py
[ "Apache-2.0" ]
Python
schedule_global_pool
<not_specific>
def schedule_global_pool(outs): """Schedule for global_pool. Parameters ---------- outs: Array of Tensor The computation graph description of global_pool in the format of an array of tensors. Returns ------- s: Schedule The computation schedule for global_pool. ...
Schedule for global_pool. Parameters ---------- outs: Array of Tensor The computation graph description of global_pool in the format of an array of tensors. Returns ------- s: Schedule The computation schedule for global_pool.
Array of Tensor The computation graph description of global_pool in the format of an array of tensors. Returns Schedule The computation schedule for global_pool.
[ "Array", "of", "Tensor", "The", "computation", "graph", "description", "of", "global_pool", "in", "the", "format", "of", "an", "array", "of", "tensors", ".", "Returns", "Schedule", "The", "computation", "schedule", "for", "global_pool", "." ]
def schedule_global_pool(outs): outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs s = tvm.create_schedule([x.op for x in outs]) def _schedule(Pool): num_thread = 8 block_x = tvm.thread_axis("blockIdx.x") block_y = tvm.thread_axis("blockIdx.y") thread_x = tvm.thre...
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Schedule for global_pool.
[ "Schedule", "for", "global_pool", "." ]
[ "\"\"\"Schedule for global_pool.\n\n Parameters\n ----------\n outs: Array of Tensor\n The computation graph description of global_pool\n in the format of an array of tensors.\n\n Returns\n -------\n s: Schedule\n The computation schedule for global_pool.\n \"\"\"", "# in...
[ { "param": "outs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "outs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
4ed5ae66c19b92e6cd5fb7d7d3a9a194a91d0f64
coderzbx/seg-mxnet
nnvm/tvm/topi/python/topi/cuda/pooling.py
[ "Apache-2.0" ]
Python
schedule_pool
<not_specific>
def schedule_pool(outs): """Schedule for pool. Parameters ---------- outs: Array of Tensor The computation graph description of pool in the format of an array of tensors. Returns ------- s: Schedule The computation schedule for pool. """ outs = [outs] if isi...
Schedule for pool. Parameters ---------- outs: Array of Tensor The computation graph description of pool in the format of an array of tensors. Returns ------- s: Schedule The computation schedule for pool.
Schedule for pool. Parameters Array of Tensor The computation graph description of pool in the format of an array of tensors. Returns Schedule The computation schedule for pool.
[ "Schedule", "for", "pool", ".", "Parameters", "Array", "of", "Tensor", "The", "computation", "graph", "description", "of", "pool", "in", "the", "format", "of", "an", "array", "of", "tensors", ".", "Returns", "Schedule", "The", "computation", "schedule", "for",...
def schedule_pool(outs): outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs s = tvm.create_schedule([x.op for x in outs]) def _schedule(PaddedInput, Pool): s[PaddedInput].compute_inline() num_thread = tvm.target.current_target(allow_none=False).max_num_threads if Pool.op ...
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Schedule for pool.
[ "Schedule", "for", "pool", "." ]
[ "\"\"\"Schedule for pool.\n\n Parameters\n ----------\n outs: Array of Tensor\n The computation graph description of pool\n in the format of an array of tensors.\n\n Returns\n -------\n s: Schedule\n The computation schedule for pool.\n \"\"\"", "# inline all one-to-one-m...
[ { "param": "outs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "outs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
a6fd691f843f9f1622e73208e8240f806fa86610
coderzbx/seg-mxnet
nnvm/tvm/topi/python/topi/rasp/depthwise_conv2d.py
[ "Apache-2.0" ]
Python
schedule_depthwise_conv2d
<not_specific>
def schedule_depthwise_conv2d(outs): """Schedule for depthwise_conv2d nchw forward. Parameters ---------- outs: Array of Tensor The computation graph description of depthwise_conv2d in the format of an array of tensors. Returns ------- s: Schedule The computation sc...
Schedule for depthwise_conv2d nchw forward. Parameters ---------- outs: Array of Tensor The computation graph description of depthwise_conv2d in the format of an array of tensors. Returns ------- s: Schedule The computation schedule for depthwise_conv2d nchw.
Schedule for depthwise_conv2d nchw forward. Parameters Array of Tensor The computation graph description of depthwise_conv2d in the format of an array of tensors. Returns Schedule The computation schedule for depthwise_conv2d nchw.
[ "Schedule", "for", "depthwise_conv2d", "nchw", "forward", ".", "Parameters", "Array", "of", "Tensor", "The", "computation", "graph", "description", "of", "depthwise_conv2d", "in", "the", "format", "of", "an", "array", "of", "tensors", ".", "Returns", "Schedule", ...
def schedule_depthwise_conv2d(outs): outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs s = tvm.create_schedule([x.op for x in outs]) def traverse(op): if tag.is_broadcast(op.tag): if op not in s.outputs: s[op].compute_inline() for tensor in op.inp...
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Schedule for depthwise_conv2d nchw forward.
[ "Schedule", "for", "depthwise_conv2d", "nchw", "forward", "." ]
[ "\"\"\"Schedule for depthwise_conv2d nchw forward.\n\n Parameters\n ----------\n outs: Array of Tensor\n The computation graph description of depthwise_conv2d\n in the format of an array of tensors.\n\n Returns\n -------\n s: Schedule\n The computation schedule for depthwise_c...
[ { "param": "outs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "outs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
0ac51f23fe9428a8ac6531bc18cc8e9fe60e3373
coderzbx/seg-mxnet
nnvm/python/nnvm/symbol.py
[ "Apache-2.0" ]
Python
_get_list_copt
<not_specific>
def _get_list_copt(self, option): """internal function to get list option""" if option == 'all': return _ctypes.c_int(0) elif option == 'read_only': return _ctypes.c_int(1) elif option == 'aux_state': return _ctypes.c_int(2) else: r...
internal function to get list option
internal function to get list option
[ "internal", "function", "to", "get", "list", "option" ]
def _get_list_copt(self, option): if option == 'all': return _ctypes.c_int(0) elif option == 'read_only': return _ctypes.c_int(1) elif option == 'aux_state': return _ctypes.c_int(2) else: raise ValueError("option need to be in {'all', 'read...
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internal function to get list option
[ "internal", "function", "to", "get", "list", "option" ]
[ "\"\"\"internal function to get list option\"\"\"" ]
[ { "param": "self", "type": null }, { "param": "option", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "option", "type": null, "docstring": null, "docstring_tokens":...
0ac51f23fe9428a8ac6531bc18cc8e9fe60e3373
coderzbx/seg-mxnet
nnvm/python/nnvm/symbol.py
[ "Apache-2.0" ]
Python
Variable
<not_specific>
def Variable(name, **kwargs): """Create a symbolic variable with specified name. Parameters ---------- name : str Name of the variable. kwargs : dict of string -> string Additional attributes to set on the variable. Returns ------- variable : Symbol The created ...
Create a symbolic variable with specified name. Parameters ---------- name : str Name of the variable. kwargs : dict of string -> string Additional attributes to set on the variable. Returns ------- variable : Symbol The created variable symbol.
Create a symbolic variable with specified name. Parameters name : str Name of the variable. kwargs : dict of string -> string Additional attributes to set on the variable. Returns variable : Symbol The created variable symbol.
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def Variable(name, **kwargs): if not isinstance(name, _base.string_types): raise TypeError('Expect a string for variable `name`') handle = _base.SymbolHandle() _base.check_call(_LIB.NNSymbolCreateVariable( _base.c_str(name), _ctypes.byref(handle))) ret = Symbol(handle) attr = AttrSco...
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Create a symbolic variable with specified name.
[ "Create", "a", "symbolic", "variable", "with", "specified", "name", "." ]
[ "\"\"\"Create a symbolic variable with specified name.\n\n Parameters\n ----------\n name : str\n Name of the variable.\n kwargs : dict of string -> string\n Additional attributes to set on the variable.\n\n Returns\n -------\n variable : Symbol\n The created variable symbo...
[ { "param": "name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
4c886b48d9c26a862506c6cccd450b005da36051
coderzbx/seg-mxnet
nnvm/tvm/topi/python/topi/x86/nn.py
[ "Apache-2.0" ]
Python
schedule_dense
<not_specific>
def schedule_dense(outs): """Schedule for dense Parameters ---------- outs: Array of Tensor The computation graph description of pool in the format of an array of tensors. Returns ------- sch: Schedule The computation schedule for the op. """ outs = [ou...
Schedule for dense Parameters ---------- outs: Array of Tensor The computation graph description of pool in the format of an array of tensors. Returns ------- sch: Schedule The computation schedule for the op.
Schedule for dense Parameters Array of Tensor The computation graph description of pool in the format of an array of tensors. Returns Schedule The computation schedule for the op.
[ "Schedule", "for", "dense", "Parameters", "Array", "of", "Tensor", "The", "computation", "graph", "description", "of", "pool", "in", "the", "format", "of", "an", "array", "of", "tensors", ".", "Returns", "Schedule", "The", "computation", "schedule", "for", "th...
def schedule_dense(outs): outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs s = tvm.create_schedule([x.op for x in outs]) def traverse(op): if tag.is_broadcast(op.tag): if op not in s.outputs: s[op].compute_inline() for tensor in op.input_tensors:...
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Schedule for dense Parameters
[ "Schedule", "for", "dense", "Parameters" ]
[ "\"\"\"Schedule for dense\n\n Parameters\n ----------\n outs: Array of Tensor\n The computation graph description of pool\n in the format of an array of tensors.\n\n Returns\n -------\n sch: Schedule\n The computation schedule for the op.\n \"\"\"", "\"\"\"Traverse op...
[ { "param": "outs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "outs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
b2a2beb6c7930fea97fd62c2d0857e160dca47e4
coderzbx/seg-mxnet
python/mxnet/seg_recordio.py
[ "Apache-2.0" ]
Python
seek
null
def seek(self, idx): """Sets the current read pointer position. This function is internally called by `read_idx(idx)` to find the current reader pointer position. It doesn't return anything.""" assert not self.writable pos = ctypes.c_size_t(self.idx[idx]) check_call(_LIB...
Sets the current read pointer position. This function is internally called by `read_idx(idx)` to find the current reader pointer position. It doesn't return anything.
Sets the current read pointer position. This function is internally called by `read_idx(idx)` to find the current reader pointer position. It doesn't return anything.
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def seek(self, idx): assert not self.writable pos = ctypes.c_size_t(self.idx[idx]) check_call(_LIB.MXRecordIOReaderSeek(self.handle, pos))
[ "def", "seek", "(", "self", ",", "idx", ")", ":", "assert", "not", "self", ".", "writable", "pos", "=", "ctypes", ".", "c_size_t", "(", "self", ".", "idx", "[", "idx", "]", ")", "check_call", "(", "_LIB", ".", "MXRecordIOReaderSeek", "(", "self", "."...
Sets the current read pointer position.
[ "Sets", "the", "current", "read", "pointer", "position", "." ]
[ "\"\"\"Sets the current read pointer position.\n\n This function is internally called by `read_idx(idx)` to find the current\n reader pointer position. It doesn't return anything.\"\"\"" ]
[ { "param": "self", "type": null }, { "param": "idx", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "idx", "type": null, "docstring": null, "docstring_tokens": []...
b2a2beb6c7930fea97fd62c2d0857e160dca47e4
coderzbx/seg-mxnet
python/mxnet/seg_recordio.py
[ "Apache-2.0" ]
Python
tell
<not_specific>
def tell(self): """Returns the current position of write head. Example usage: ---------- >>> record = mx.seg_recordio.MXIndexedSegRecordIO('tmp.idx', 'tmp.rec', 'w') >>> print(record.tell()) 0 >>> for i in range(5): ... record.write_idx(i, 'record_%d'...
Returns the current position of write head. Example usage: ---------- >>> record = mx.seg_recordio.MXIndexedSegRecordIO('tmp.idx', 'tmp.rec', 'w') >>> print(record.tell()) 0 >>> for i in range(5): ... record.write_idx(i, 'record_%d'%i) ... print(r...
Returns the current position of write head. Example usage.
[ "Returns", "the", "current", "position", "of", "write", "head", ".", "Example", "usage", "." ]
def tell(self): assert self.writable pos = ctypes.c_size_t() check_call(_LIB.MXRecordIOWriterTell(self.handle, ctypes.byref(pos))) return pos.value
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Returns the current position of write head.
[ "Returns", "the", "current", "position", "of", "write", "head", "." ]
[ "\"\"\"Returns the current position of write head.\n\n Example usage:\n ----------\n >>> record = mx.seg_recordio.MXIndexedSegRecordIO('tmp.idx', 'tmp.rec', 'w')\n >>> print(record.tell())\n 0\n >>> for i in range(5):\n ... record.write_idx(i, 'record_%d'%i)\n ...
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
0970b76142ae4d9e8437a734839a7281ad614e74
coderzbx/seg-mxnet
nnvm/tvm/topi/python/topi/x86/injective.py
[ "Apache-2.0" ]
Python
schedule_injective
<not_specific>
def schedule_injective(outs): """X86 schedule for injective op. Parameters ---------- outs: Array of Tensor The computation graph description of injective in the format of an array of tensors. Returns ------- sch: Schedule The computation schedule for the op. ...
X86 schedule for injective op. Parameters ---------- outs: Array of Tensor The computation graph description of injective in the format of an array of tensors. Returns ------- sch: Schedule The computation schedule for the op.
X86 schedule for injective op. Parameters Array of Tensor The computation graph description of injective in the format of an array of tensors. Returns Schedule The computation schedule for the op.
[ "X86", "schedule", "for", "injective", "op", ".", "Parameters", "Array", "of", "Tensor", "The", "computation", "graph", "description", "of", "injective", "in", "the", "format", "of", "an", "array", "of", "tensors", ".", "Returns", "Schedule", "The", "computati...
def schedule_injective(outs): outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs x = outs[0] s = tvm.create_schedule([x.op for x in outs]) tvm.schedule.AutoInlineInjective(s) if len(s[x].op.axis) == 4: n, c, _, _ = s[x].op.axis fused = s[x].fuse(n, c) s[x].parall...
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X86 schedule for injective op.
[ "X86", "schedule", "for", "injective", "op", "." ]
[ "\"\"\"X86 schedule for injective op.\n\n Parameters\n ----------\n outs: Array of Tensor\n The computation graph description of injective in the format\n of an array of tensors.\n\n Returns\n -------\n sch: Schedule\n The computation schedule for the op.\n \"\"\"", "...
[ { "param": "outs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "outs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
851a00db0a481acd54cd9f9cdbef406771b8d564
coderzbx/seg-mxnet
nnvm/tvm/topi/python/topi/cuda/depthwise_conv2d.py
[ "Apache-2.0" ]
Python
schedule_depthwise_conv2d_nhwc
<not_specific>
def schedule_depthwise_conv2d_nhwc(outs): """Schedule for depthwise_conv2d nhwc forward. Parameters ---------- outs: Array of Tensor The computation graph description of depthwise_conv2d in the format of an array of tensors. Returns ------- s: Schedule The computati...
Schedule for depthwise_conv2d nhwc forward. Parameters ---------- outs: Array of Tensor The computation graph description of depthwise_conv2d in the format of an array of tensors. Returns ------- s: Schedule The computation schedule for depthwise_conv2d nhwc.
Schedule for depthwise_conv2d nhwc forward. Parameters Array of Tensor The computation graph description of depthwise_conv2d in the format of an array of tensors. Returns Schedule The computation schedule for depthwise_conv2d nhwc.
[ "Schedule", "for", "depthwise_conv2d", "nhwc", "forward", ".", "Parameters", "Array", "of", "Tensor", "The", "computation", "graph", "description", "of", "depthwise_conv2d", "in", "the", "format", "of", "an", "array", "of", "tensors", ".", "Returns", "Schedule", ...
def schedule_depthwise_conv2d_nhwc(outs): outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs s = tvm.create_schedule([x.op for x in outs]) def _schedule(temp, Filter, DepthwiseConv2d): s[temp].compute_inline() FS = s.cache_read(Filter, "shared", [DepthwiseConv2d]) if Dept...
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Schedule for depthwise_conv2d nhwc forward.
[ "Schedule", "for", "depthwise_conv2d", "nhwc", "forward", "." ]
[ "\"\"\"Schedule for depthwise_conv2d nhwc forward.\n\n Parameters\n ----------\n outs: Array of Tensor\n The computation graph description of depthwise_conv2d\n in the format of an array of tensors.\n\n Returns\n -------\n s: Schedule\n The computation schedule for depthwise_c...
[ { "param": "outs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "outs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
e05df7181544aacba12b2d8239c1afbbcad50651
coderzbx/seg-mxnet
nnvm/tvm/python/tvm/contrib/xcode.py
[ "Apache-2.0" ]
Python
codesign
null
def codesign(lib): """Codesign the shared libary This is an required step for library to be loaded in the app. Parameters ---------- lib : The path to the library. """ if "TVM_IOS_CODESIGN" not in os.environ: raise RuntimeError("Require environment variable TVM_IOS_CODESIGN " ...
Codesign the shared libary This is an required step for library to be loaded in the app. Parameters ---------- lib : The path to the library.
Codesign the shared libary This is an required step for library to be loaded in the app. Parameters lib : The path to the library.
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def codesign(lib): if "TVM_IOS_CODESIGN" not in os.environ: raise RuntimeError("Require environment variable TVM_IOS_CODESIGN " " to be the signature") signature = os.environ["TVM_IOS_CODESIGN"] cmd = ["codesign", "--force", "--sign", signature] cmd += [lib] proc =...
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Codesign the shared libary This is an required step for library to be loaded in the app.
[ "Codesign", "the", "shared", "libary", "This", "is", "an", "required", "step", "for", "library", "to", "be", "loaded", "in", "the", "app", "." ]
[ "\"\"\"Codesign the shared libary\n\n This is an required step for library to be loaded in\n the app.\n\n Parameters\n ----------\n lib : The path to the library.\n \"\"\"" ]
[ { "param": "lib", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "lib", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
cd3969ea0eec17222c5d230cc66620f0ea1558d9
yufengliang/mbxaspy
utils.py
[ "Apache-2.0" ]
Python
is_valid_variable_name
<not_specific>
def is_valid_variable_name(name): """test if name is a valid python variable name""" try: parse('{} = None'.format(name)) return True except (SyntaxError, ValueError, TypeError) as err: return False
test if name is a valid python variable name
test if name is a valid python variable name
[ "test", "if", "name", "is", "a", "valid", "python", "variable", "name" ]
def is_valid_variable_name(name): try: parse('{} = None'.format(name)) return True except (SyntaxError, ValueError, TypeError) as err: return False
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test if name is a valid python variable name
[ "test", "if", "name", "is", "a", "valid", "python", "variable", "name" ]
[ "\"\"\"test if name is a valid python variable name\"\"\"" ]
[ { "param": "name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
cd3969ea0eec17222c5d230cc66620f0ea1558d9
yufengliang/mbxaspy
utils.py
[ "Apache-2.0" ]
Python
find_nocc
<not_specific>
def find_nocc(two_arr, n): """ Given two sorted arrays of the SAME lengths and a number, find the nth smallest number a_n and use two indices to indicate the numbers that are no larger than a_n. n can be real. Take the floor. """ l = len(two_arr[0]) if n >= 2 * l: return l, l if n ...
Given two sorted arrays of the SAME lengths and a number, find the nth smallest number a_n and use two indices to indicate the numbers that are no larger than a_n. n can be real. Take the floor.
Given two sorted arrays of the SAME lengths and a number, find the nth smallest number a_n and use two indices to indicate the numbers that are no larger than a_n. n can be real. Take the floor.
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def find_nocc(two_arr, n): l = len(two_arr[0]) if n >= 2 * l: return l, l if n == 0: return 0, 0 res, n = n % 1, int(n) lo, hi = max(0, n - l - 1), min(l - 1, n - 1) while lo <= hi: mid = int((lo + hi) / 2) if mid + 1 < l and n - mid - 2 >= 0: if two_arr[0][mid + 1]...
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Given two sorted arrays of the SAME lengths and a number, find the nth smallest number a_n and use two indices to indicate the numbers that are no larger than a_n.
[ "Given", "two", "sorted", "arrays", "of", "the", "SAME", "lengths", "and", "a", "number", "find", "the", "nth", "smallest", "number", "a_n", "and", "use", "two", "indices", "to", "indicate", "the", "numbers", "that", "are", "no", "larger", "than", "a_n", ...
[ "\"\"\"\n Given two sorted arrays of the SAME lengths and a number,\n find the nth smallest number a_n and use two indices to indicate\n the numbers that are no larger than a_n.\n\n n can be real. Take the floor.\n \"\"\"", "# image mid is the right answer" ]
[ { "param": "two_arr", "type": null }, { "param": "n", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "two_arr", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "n", "type": null, "docstring": null, "docstring_tokens": [...
c367b9dd81655f7ad51960b2415dd963fb41d6a5
yufengliang/mbxaspy
defs.py
[ "Apache-2.0" ]
Python
import_from_iptblk
null
def import_from_iptblk(self, tmp_iptblk): """ import atomic species and positions (names) from TMP_INPUT by shirley_xas """ para = self.para self.atomic_species = atomic_species_to_list(tmp_iptblk['TMP_ATOMIC_SPECIES']) # element pseudopotential_file self.atomic_pos = atomic_position...
import atomic species and positions (names) from TMP_INPUT by shirley_xas
import atomic species and positions (names) from TMP_INPUT by shirley_xas
[ "import", "atomic", "species", "and", "positions", "(", "names", ")", "from", "TMP_INPUT", "by", "shirley_xas" ]
def import_from_iptblk(self, tmp_iptblk): para = self.para self.atomic_species = atomic_species_to_list(tmp_iptblk['TMP_ATOMIC_SPECIES']) self.atomic_pos = atomic_positions_to_list(tmp_iptblk['TMP_ATOMIC_POSITIONS']) self.nspecies = len(self.atomic_species) self.natom ...
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import atomic species and positions (names) from TMP_INPUT by shirley_xas
[ "import", "atomic", "species", "and", "positions", "(", "names", ")", "from", "TMP_INPUT", "by", "shirley_xas" ]
[ "\"\"\" import atomic species and positions (names) from TMP_INPUT by shirley_xas \"\"\"", "# element pseudopotential_file", "# element x y z", "# para.print(self.atomic_species) # debug", "# para.print(self.atomic_pos) # debug", "# identify the excited atom if any" ]
[ { "param": "self", "type": null }, { "param": "tmp_iptblk", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "tmp_iptblk", "type": null, "docstring": null, "docstring_toke...
c367b9dd81655f7ad51960b2415dd963fb41d6a5
yufengliang/mbxaspy
defs.py
[ "Apache-2.0" ]
Python
import_l_qij
null
def import_l_qij(self): """ import Q_int for all ground-state atoms in the supercell """ para = self.para scf = self.scf para.print(' {0:6}{1:<30}{2:<20}'.format('Kind', 'UPF', 'Beta L')) # Import l and qij for each species of atom for i in range(self.nspecies): ...
import Q_int for all ground-state atoms in the supercell
import Q_int for all ground-state atoms in the supercell
[ "import", "Q_int", "for", "all", "ground", "-", "state", "atoms", "in", "the", "supercell" ]
def import_l_qij(self): para = self.para scf = self.scf para.print(' {0:6}{1:<30}{2:<20}'.format('Kind', 'UPF', 'Beta L')) for i in range(self.nspecies): pseudo_fname = scf.tmp_iptblk['TMP_PSEUDO_DIR'] + '/' + self.atomic_species[i][1] l, qij, errmsg = re...
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import Q_int for all ground-state atoms in the supercell
[ "import", "Q_int", "for", "all", "ground", "-", "state", "atoms", "in", "the", "supercell" ]
[ "\"\"\" import Q_int for all ground-state atoms in the supercell \"\"\"", "# Import l and qij for each species of atom", "# para.print(l) # debug", "# para.print(qij) # debug", "# print out PAW atom information", "# Calculate # projectors for each kind of atom", "# Calculate total # of projectors in the...
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
c367b9dd81655f7ad51960b2415dd963fb41d6a5
yufengliang/mbxaspy
defs.py
[ "Apache-2.0" ]
Python
find_icore
null
def find_icore(self): """ Calculate the index of this excited atom among all the excited atoms """ self.ind_excitation = [0] * self.natom for key in self.scf.iptblk: if 'IND_EXCITATION' in key: self.ind_excitation[get_index(key) - 1] = int(self.scf.iptblk[key]) ...
Calculate the index of this excited atom among all the excited atoms
Calculate the index of this excited atom among all the excited atoms
[ "Calculate", "the", "index", "of", "this", "excited", "atom", "among", "all", "the", "excited", "atoms" ]
def find_icore(self): self.ind_excitation = [0] * self.natom for key in self.scf.iptblk: if 'IND_EXCITATION' in key: self.ind_excitation[get_index(key) - 1] = int(self.scf.iptblk[key]) self.ncore = sum(self.ind_excitation) if self.x > 0: self.icor...
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Calculate the index of this excited atom among all the excited atoms
[ "Calculate", "the", "index", "of", "this", "excited", "atom", "among", "all", "the", "excited", "atoms" ]
[ "\"\"\" Calculate the index of this excited atom among all the excited atoms \"\"\"", "# self.para.print(self.ind_excitation) # debug" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
c367b9dd81655f7ad51960b2415dd963fb41d6a5
yufengliang/mbxaspy
defs.py
[ "Apache-2.0" ]
Python
input_sij
null
def input_sij(self): """ input the atomic overlap term sij There should be one sij file for one UPF file of each excited atom. For example: O.pbe-van-yufengl-1s1.sij => O.pbe-van-yufengl-1s1.UPF So we would need to look for sij for a given UPF """ scf = ...
input the atomic overlap term sij There should be one sij file for one UPF file of each excited atom. For example: O.pbe-van-yufengl-1s1.sij => O.pbe-van-yufengl-1s1.UPF So we would need to look for sij for a given UPF
input the atomic overlap term sij There should be one sij file for one UPF file of each excited atom.
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def input_sij(self): scf = self.scf para = self.para if self.x >= 0: fname = self.atomic_species[self.xs][1] fname = scf.tmp_iptblk['TMP_PSEUDO_DIR'] + '/' + os.path.splitext(fname)[0] + '.sij' try: fh = open(fname, 'r') except IOE...
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input the atomic overlap term sij There should be one sij file for one UPF file of each excited atom.
[ "input", "the", "atomic", "overlap", "term", "sij", "There", "should", "be", "one", "sij", "file", "for", "one", "UPF", "file", "of", "each", "excited", "atom", "." ]
[ "\"\"\" \n input the atomic overlap term sij\n\n There should be one sij file for one UPF file of each excited atom.\n For example:\n O.pbe-van-yufengl-1s1.sij => O.pbe-van-yufengl-1s1.UPF\n So we would need to look for sij for a given UPF\n \"\"\"", "# if there exists an...
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
b58411530f694777f421d0022611122a2be23592
yufengliang/mbxaspy
analysis.py
[ "Apache-2.0" ]
Python
xatom
<not_specific>
def xatom(proj, xmat): """ Given < beta | nk > and < nk | r | phi_c >, obtain sum p = rx, ry, rz | sum_{nk} < beta | nk > < nk | x | phi_c > | ^ 2 This should reflect which atom is excited. """ beta_nk = proj.beta_nk nbnd = min(beta_nk.shape[1], xmat.shape[0]) beta_c = sp.array([0....
Given < beta | nk > and < nk | r | phi_c >, obtain sum p = rx, ry, rz | sum_{nk} < beta | nk > < nk | x | phi_c > | ^ 2 This should reflect which atom is excited.
This should reflect which atom is excited.
[ "This", "should", "reflect", "which", "atom", "is", "excited", "." ]
def xatom(proj, xmat): beta_nk = proj.beta_nk nbnd = min(beta_nk.shape[1], xmat.shape[0]) beta_c = sp.array([0.0] * proj.nproj) for ixyz in range(3): beta_c += sp.array( abs( sp.matrix(beta_nk[:, : nbnd]) * sp.matrix(xmat[: nbnd, 0, ixyz]).T ) ) [:, 0] ** 2 atom_proj = [0.0] * proj.natom ...
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Given < beta | nk > and < nk | r | phi_c >, obtain sum p = rx, ry, rz | sum_{nk} < beta | nk > < nk | x | phi_c > | ^ 2
[ "Given", "<", "beta", "|", "nk", ">", "and", "<", "nk", "|", "r", "|", "phi_c", ">", "obtain", "sum", "p", "=", "rx", "ry", "rz", "|", "sum_", "{", "nk", "}", "<", "beta", "|", "nk", ">", "<", "nk", "|", "x", "|", "phi_c", ">", "|", "^", ...
[ "\"\"\"\n Given < beta | nk > and < nk | r | phi_c >, obtain\n sum p = rx, ry, rz\n | sum_{nk} < beta | nk > < nk | x | phi_c > | ^ 2\n\n This should reflect which atom is excited.\n \"\"\"", "# why so ugly ?! *** " ]
[ { "param": "proj", "type": null }, { "param": "xmat", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "proj", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "xmat", "type": null, "docstring": null, "docstring_tokens": [...
cc86481f20c9b0db449dfbbb5699f3be5e4f934e
yufengliang/mbxaspy
io_mod.py
[ "Apache-2.0" ]
Python
input_from_binary
<not_specific>
def input_from_binary(fhandle, data_type, ndata, offset): """ input data from a binary file Args: fhandle: file handle. The file needs to be opened first. data_type: 'float', 'double', 'complex'. ndata: length of the data measured in data_type offset: start to read at the offse...
input data from a binary file Args: fhandle: file handle. The file needs to be opened first. data_type: 'float', 'double', 'complex'. ndata: length of the data measured in data_type offset: start to read at the offset measured in data_type. count from the head of f...
input data from a binary file
[ "input", "data", "from", "a", "binary", "file" ]
def input_from_binary(fhandle, data_type, ndata, offset): if not data_type in data_set: raise TypeError(' data_type must be in ' + str(set(data_set)) + '.' ) pos = fhandle.tell() fhandle.seek(offset * data_set[data_type][0]) data = fhandle.read(ndata * data_set[data_type][0]) data_len = dat...
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input data from a binary file
[ "input", "data", "from", "a", "binary", "file" ]
[ "\"\"\" input data from a binary file \n\n Args:\n fhandle: file handle. The file needs to be opened first.\n data_type: 'float', 'double', 'complex'.\n ndata: length of the data measured in data_type\n offset: start to read at the offset measured in data_type.\n count ...
[ { "param": "fhandle", "type": null }, { "param": "data_type", "type": null }, { "param": "ndata", "type": null }, { "param": "offset", "type": null } ]
{ "returns": [ { "docstring": "a list of data of specified data_type", "docstring_tokens": [ "a", "list", "of", "data", "of", "specified", "data_type" ], "type": null } ], "raises": [], "params": [ { "identifier": ...
cc86481f20c9b0db449dfbbb5699f3be5e4f934e
yufengliang/mbxaspy
io_mod.py
[ "Apache-2.0" ]
Python
convert_val
<not_specific>
def convert_val(val_str, val): """ Given a string, convert into the correct data type """ if val is bool: if 'true' in val_str.lower(): val_str = 'true' else: val_str = '' # otherwise set to false val_type = val try: return val_type(val_str) except ValueError: # Can ...
Given a string, convert into the correct data type
Given a string, convert into the correct data type
[ "Given", "a", "string", "convert", "into", "the", "correct", "data", "type" ]
def convert_val(val_str, val): if val is bool: if 'true' in val_str.lower(): val_str = 'true' else: val_str = '' val_type = val try: return val_type(val_str) except ValueError: return val_type(float(val_str))
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Given a string, convert into the correct data type
[ "Given", "a", "string", "convert", "into", "the", "correct", "data", "type" ]
[ "\"\"\" Given a string, convert into the correct data type \"\"\"", "# otherwise set to false", "# Can it be a float ?" ]
[ { "param": "val_str", "type": null }, { "param": "val", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "val_str", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "val", "type": null, "docstring": null, "docstring_tokens":...
cc86481f20c9b0db449dfbbb5699f3be5e4f934e
yufengliang/mbxaspy
io_mod.py
[ "Apache-2.0" ]
Python
list2str_1d
<not_specific>
def list2str_1d(nums, mid = -1): """ Give a list of nums, output the head, the middle, and the tail of it with nice format. Return the formatted string. Args: mid: define the middle point you are interested in """ nvis = 3 # numbers printed out in each part l = len(nums) mid = mi...
Give a list of nums, output the head, the middle, and the tail of it with nice format. Return the formatted string. Args: mid: define the middle point you are interested in
Give a list of nums, output the head, the middle, and the tail of it with nice format. Return the formatted string. define the middle point you are interested in
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def list2str_1d(nums, mid = -1): nvis = 3 l = len(nums) mid = mid if mid > 0 else l / 2 fmtstr = '{0:.4f} ' resstr = '' for i in range(min(nvis, l)): resstr += fmtstr.format(nums[i]) irange = range(max(nvis + 1, int(mid - nvis / 2)), min(l, int(mid + nvis / 2 + 1))) if len(iran...
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Give a list of nums, output the head, the middle, and the tail of it with nice format.
[ "Give", "a", "list", "of", "nums", "output", "the", "head", "the", "middle", "and", "the", "tail", "of", "it", "with", "nice", "format", "." ]
[ "\"\"\" \n Give a list of nums, output the head, the middle, and the tail of it\n with nice format. Return the formatted string.\n\n Args:\n mid: define the middle point you are interested in\n \"\"\"", "# numbers printed out in each part" ]
[ { "param": "nums", "type": null }, { "param": "mid", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "nums", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "mid", "type": null, "docstring": null, "docstring_tokens": []...
cc86481f20c9b0db449dfbbb5699f3be5e4f934e
yufengliang/mbxaspy
io_mod.py
[ "Apache-2.0" ]
Python
eigvec2str
<not_specific>
def eigvec2str(eigvec, m, n, nctr, nvis = 6, npc = 6, iws = ' '): """ Output some prominent matrix elements for an eigenvector matrix eigvec is given as a 1D array: [ <B_1|1k>, <B_1|2k>, <B_2|1k>, <B_2|2k>] which corresponds to such a matrix (m rows x n cols): <B_1|1k> <B_1|2k> <B_2|...
Output some prominent matrix elements for an eigenvector matrix eigvec is given as a 1D array: [ <B_1|1k>, <B_1|2k>, <B_2|1k>, <B_2|2k>] which corresponds to such a matrix (m rows x n cols): <B_1|1k> <B_1|2k> <B_2|1k> <B_2|2k> nctr: list states around the center nctr nvis: numb...
Output some prominent matrix elements for an eigenvector matrix eigvec is given as a 1D array: [ , , , ] which corresponds to such a matrix (m rows x n cols): list states around the center nctr nvis: number of printed out states npc: number of principal components iws: initial white spaces for indentation
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def eigvec2str(eigvec, m, n, nctr, nvis = 6, npc = 6, iws = ' '): resstr = iws + '{0:<10}{1}\n'.format('norm', 'principal components') for j in range(max(0, nctr - int(nvis / 2) + 1), min(n, nctr + int(nvis / 2) + 1)): eabs = [ abs(eigvec[i * n + j]) ** 2 for i in range(m) ] norm = sum(eabs) ...
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Output some prominent matrix elements for an eigenvector matrix eigvec is given as a 1D array: [ <B_1|1k>, <B_1|2k>, <B_2|1k>, <B_2|2k>]
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[ "\"\"\"\n Output some prominent matrix elements for an eigenvector matrix\n \n eigvec is given as a 1D array:\n [ <B_1|1k>, <B_1|2k>, <B_2|1k>, <B_2|2k>]\n\n which corresponds to such a matrix (m rows x n cols):\n <B_1|1k> <B_1|2k>\n <B_2|1k> <B_2|2k>\n\n nctr: list states around the cente...
[ { "param": "eigvec", "type": null }, { "param": "m", "type": null }, { "param": "n", "type": null }, { "param": "nctr", "type": null }, { "param": "nvis", "type": null }, { "param": "npc", "type": null }, { "param": "iws", "type": null ...
{ "returns": [], "raises": [], "params": [ { "identifier": "eigvec", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "m", "type": null, "docstring": null, "docstring_tokens": []...
cc86481f20c9b0db449dfbbb5699f3be5e4f934e
yufengliang/mbxaspy
io_mod.py
[ "Apache-2.0" ]
Python
atomic_positions_to_list
<not_specific>
def atomic_positions_to_list(apos_str): """ Convert a atomic_positions block (as in Qespresso) into a list like: [['Pb', '0.0', '0.0', '0.0'], ['Br', '0.0', '0.0', '0.5'], ...] Most interested in the atoms' names rather than their positions """ res = [] for l in apos_str.split('\n'): ...
Convert a atomic_positions block (as in Qespresso) into a list like: [['Pb', '0.0', '0.0', '0.0'], ['Br', '0.0', '0.0', '0.5'], ...] Most interested in the atoms' names rather than their positions
Most interested in the atoms' names rather than their positions
[ "Most", "interested", "in", "the", "atoms", "'", "names", "rather", "than", "their", "positions" ]
def atomic_positions_to_list(apos_str): res = [] for l in apos_str.split('\n'): words = l.split() if len(words) >= 4 and len(words[0]) < elem_maxl: res.append(words) return res
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Convert a atomic_positions block (as in Qespresso) into a list like: [['Pb', '0.0', '0.0', '0.0'], ['Br', '0.0', '0.0', '0.5'], ...]
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[ "\"\"\"\n Convert a atomic_positions block (as in Qespresso) into a list like:\n\n [['Pb', '0.0', '0.0', '0.0'], ['Br', '0.0', '0.0', '0.5'], ...]\n\n Most interested in the atoms' names rather than their positions \n \"\"\"", "# *** There should be more robust sanity checks: check elements" ]
[ { "param": "apos_str", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "apos_str", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
cc86481f20c9b0db449dfbbb5699f3be5e4f934e
yufengliang/mbxaspy
io_mod.py
[ "Apache-2.0" ]
Python
read_qij_from_upf
<not_specific>
def read_qij_from_upf(upf_fname): """ Given a PAW/ultrasoft pseudopotential in UPF format, find the projectors' angular momenta and the corresponding Q_int matrices in the file. """ l = [] # angular momentum number qij = [] # Q_int matrix i, j = 0, 0 errmsg = '' fh = [] try: ...
Given a PAW/ultrasoft pseudopotential in UPF format, find the projectors' angular momenta and the corresponding Q_int matrices in the file.
Given a PAW/ultrasoft pseudopotential in UPF format, find the projectors' angular momenta and the corresponding Q_int matrices in the file.
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def read_qij_from_upf(upf_fname): l = [] qij = [] i, j = 0, 0 errmsg = '' fh = [] try: fh = open(upf_fname, 'r') except IOError: errmsg = 'cannot open UPF file: ' + str(upf_fname) for line in fh: words = line.split() if len(words) >= 4 and words[2 : 4]...
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Given a PAW/ultrasoft pseudopotential in UPF format, find the projectors' angular momenta and the corresponding Q_int matrices in the file.
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[ "\"\"\"\n Given a PAW/ultrasoft pseudopotential in UPF format, find the projectors' angular momenta\n and the corresponding Q_int matrices in the file.\n\n \"\"\"", "# angular momentum number", "# Q_int matrix", "# if first time, initialize the qij matrix" ]
[ { "param": "upf_fname", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "upf_fname", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
ccbc1085cbed3312d161c4c73dcf822c91318096
yufengliang/mbxaspy
xi.py
[ "Apache-2.0" ]
Python
compute_full_sij
null
def compute_full_sij(fproj): """ Given the final proj_class, calculate the full S_ij matrix for each kind of atom ground-state atom: sij is just qij excited sij stored under proj of fscf """ fproj.full_sij = [] for kind in range(fproj.nspecies): full_sij = sp.matrix(sp.zeros([fp...
Given the final proj_class, calculate the full S_ij matrix for each kind of atom ground-state atom: sij is just qij excited sij stored under proj of fscf
Given the final proj_class, calculate the full S_ij matrix for each kind of atom ground-state atom: sij is just qij excited sij stored under proj of fscf
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def compute_full_sij(fproj): fproj.full_sij = [] for kind in range(fproj.nspecies): full_sij = sp.matrix(sp.zeros([fproj.nprojs[kind], fproj.nprojs[kind]])) if fproj.xs == kind: sij = fproj.sij else: sij = fproj.qij[kind] l_offset1 = il1 = 0 for l1...
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Given the final proj_class, calculate the full S_ij matrix for each kind of atom
[ "Given", "the", "final", "proj_class", "calculate", "the", "full", "S_ij", "matrix", "for", "each", "kind", "of", "atom" ]
[ "\"\"\"\n Given the final proj_class, calculate the full S_ij matrix\n for each kind of atom\n\n ground-state atom: sij is just qij\n excited sij stored under proj of fscf\n \"\"\"", "# Extract sij for this kind of atom", "# if it is an excited atom", "# fproj.para.print(sij) # debug", "# fpr...
[ { "param": "fproj", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "fproj", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
ccbc1085cbed3312d161c4c73dcf822c91318096
yufengliang/mbxaspy
xi.py
[ "Apache-2.0" ]
Python
compute_xi
<not_specific>
def compute_xi(iscf, fscf): """ compute the xi matrix using two given scfs """ if userin.scf_type == 'shirley_xas': # The pseudo part: xi_{mn}^PS = < nk | B_j > < B_j | ~ B_i > < ~ B_i | ~mk > # xi = iscf.obf.eigvec.H * fscf.obf.overlap * fscf.obf.eigvec # All 3 must be sp.matrix...
compute the xi matrix using two given scfs
compute the xi matrix using two given scfs
[ "compute", "the", "xi", "matrix", "using", "two", "given", "scfs" ]
def compute_xi(iscf, fscf): if userin.scf_type == 'shirley_xas': xi = iscf.obf.eigvec[:, : iscf.nbnd_use].H \ * fscf.obf.overlap \ * fscf.obf.eigvec[:, : fscf.nbnd_use] proj_offset = 0 proj = fscf.proj if userin.do_paw_correction: for ...
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compute the xi matrix using two given scfs
[ "compute", "the", "xi", "matrix", "using", "two", "given", "scfs" ]
[ "\"\"\" \n compute the xi matrix using two given scfs\n \"\"\"", "# The pseudo part: xi_{mn}^PS = < nk | B_j > < B_j | ~ B_i > < ~ B_i | ~mk >", "# xi = iscf.obf.eigvec.H * fscf.obf.overlap * fscf.obf.eigvec # All 3 must be sp.matrix", "# PAW corrections:", "# \\sum_{I, l, l'} < nk | beta_Il > S_...
[ { "param": "iscf", "type": null }, { "param": "fscf", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "iscf", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "fscf", "type": null, "docstring": null, "docstring_tokens": [...
ccbc1085cbed3312d161c4c73dcf822c91318096
yufengliang/mbxaspy
xi.py
[ "Apache-2.0" ]
Python
plot_xi
null
def plot_xi(xi): """ plot a heap map for a complex matrix xi Take the abs of each element and plot sp: scipy or numpy """ heatmap = plt.imshow(abs(sp.array(xi)), cmap = 'seismic') plt.axis([0, xi.shape[1], 0, xi.shape[0]]) plt.axes().set_aspect('equal') plt.savefig('xi.png', for...
plot a heap map for a complex matrix xi Take the abs of each element and plot sp: scipy or numpy
plot a heap map for a complex matrix xi Take the abs of each element and plot scipy or numpy
[ "plot", "a", "heap", "map", "for", "a", "complex", "matrix", "xi", "Take", "the", "abs", "of", "each", "element", "and", "plot", "scipy", "or", "numpy" ]
def plot_xi(xi): heatmap = plt.imshow(abs(sp.array(xi)), cmap = 'seismic') plt.axis([0, xi.shape[1], 0, xi.shape[0]]) plt.axes().set_aspect('equal') plt.savefig('xi.png', format = 'png') plt.close()
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plot a heap map for a complex matrix xi Take the abs of each element and plot
[ "plot", "a", "heap", "map", "for", "a", "complex", "matrix", "xi", "Take", "the", "abs", "of", "each", "element", "and", "plot" ]
[ "\"\"\"\n plot a heap map for a complex matrix xi\n Take the abs of each element and plot\n \n sp: scipy or numpy\n \"\"\"" ]
[ { "param": "xi", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "xi", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
ccbc1085cbed3312d161c4c73dcf822c91318096
yufengliang/mbxaspy
xi.py
[ "Apache-2.0" ]
Python
eig_analysis_xi
<not_specific>
def eig_analysis_xi(xi, postfix = ''): """ Analyze the eigenvalues of the transformation matrix xi sp: scipy or numpy la: linalg """ size = min(xi.shape) xi_eigval, xi_eigvec = la.eig(xi[0 : size, 0 : size]) # Now I plot the abs of eigenvalues out_eigval = sorted(abs(xi_eigval), rev...
Analyze the eigenvalues of the transformation matrix xi sp: scipy or numpy la: linalg
Analyze the eigenvalues of the transformation matrix xi sp: scipy or numpy la: linalg
[ "Analyze", "the", "eigenvalues", "of", "the", "transformation", "matrix", "xi", "sp", ":", "scipy", "or", "numpy", "la", ":", "linalg" ]
def eig_analysis_xi(xi, postfix = ''): size = min(xi.shape) xi_eigval, xi_eigvec = la.eig(xi[0 : size, 0 : size]) out_eigval = sorted(abs(xi_eigval), reverse = True) plt.stem(out_eigval) plt.xlim([-1, size]) plt.savefig('xi_eig{0}.png'.format(postfix), format = 'png') plt.close() sp.save...
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Analyze the eigenvalues of the transformation matrix xi sp: scipy or numpy la: linalg
[ "Analyze", "the", "eigenvalues", "of", "the", "transformation", "matrix", "xi", "sp", ":", "scipy", "or", "numpy", "la", ":", "linalg" ]
[ "\"\"\"\n Analyze the eigenvalues of the transformation matrix xi\n\n sp: scipy or numpy\n la: linalg\n \"\"\"", "# Now I plot the abs of eigenvalues", "#plt.savefig('test_xi_eig.eps', format = 'eps', dpi = 1000)", "# Analyze eigenvalues and return a message" ]
[ { "param": "xi", "type": null }, { "param": "postfix", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "xi", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "postfix", "type": null, "docstring": null, "docstring_tokens": ...
90bec505823a1c42e9083f1c94b95aba7eef8fb4
yufengliang/mbxaspy
para_defs.py
[ "Apache-2.0" ]
Python
sk_info
null
def sk_info(self): """ collect and print out the spin-kpoint tuples on each pool """ para = self.para if self.rootcomm: self.sk_list_all = self.rootcomm.gather(self.sk_list, root = 0) self.sk_list_all = self.rootcomm.bcast(self.sk_list_all, root = 0) else: ...
collect and print out the spin-kpoint tuples on each pool
collect and print out the spin-kpoint tuples on each pool
[ "collect", "and", "print", "out", "the", "spin", "-", "kpoint", "tuples", "on", "each", "pool" ]
def sk_info(self): para = self.para if self.rootcomm: self.sk_list_all = self.rootcomm.gather(self.sk_list, root = 0) self.sk_list_all = self.rootcomm.bcast(self.sk_list_all, root = 0) else: self.sk_list_all = [self.sk_list] self.sk_list_maxl = max([le...
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collect and print out the spin-kpoint tuples on each pool
[ "collect", "and", "print", "out", "the", "spin", "-", "kpoint", "tuples", "on", "each", "pool" ]
[ "\"\"\" collect and print out the spin-kpoint tuples on each pool \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
90bec505823a1c42e9083f1c94b95aba7eef8fb4
yufengliang/mbxaspy
para_defs.py
[ "Apache-2.0" ]
Python
log
null
def log(self, msg = '', flush = False): """ log messages from each proc and print on demand """ if len(msg) > 0: self.msg += msg + '\n' if flush: if self.rootcomm: msgs = self.rootcomm.gather(self.msg, root = 0) if self.rootcomm.Get_rank()...
log messages from each proc and print on demand
log messages from each proc and print on demand
[ "log", "messages", "from", "each", "proc", "and", "print", "on", "demand" ]
def log(self, msg = '', flush = False): if len(msg) > 0: self.msg += msg + '\n' if flush: if self.rootcomm: msgs = self.rootcomm.gather(self.msg, root = 0) if self.rootcomm.Get_rank() == 0: for i, m in enumerate(msgs): ...
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log messages from each proc and print on demand
[ "log", "messages", "from", "each", "proc", "and", "print", "on", "demand" ]
[ "\"\"\" log messages from each proc and print on demand \"\"\"" ]
[ { "param": "self", "type": null }, { "param": "msg", "type": null }, { "param": "flush", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "msg", "type": null, "docstring": null, "docstring_tokens": []...
90bec505823a1c42e9083f1c94b95aba7eef8fb4
yufengliang/mbxaspy
para_defs.py
[ "Apache-2.0" ]
Python
log
null
def log(self, msg = '', flush = False): """ log messages from each proc and print on demand """ if len(msg) > 0: self.msg += msg + '\n' if flush: if self.comm: msgs = self.comm.gather(self.msg, root = 0) if self.comm.Get_rank() == 0: ...
log messages from each proc and print on demand
log messages from each proc and print on demand
[ "log", "messages", "from", "each", "proc", "and", "print", "on", "demand" ]
def log(self, msg = '', flush = False): if len(msg) > 0: self.msg += msg + '\n' if flush: if self.comm: msgs = self.comm.gather(self.msg, root = 0) if self.comm.Get_rank() == 0: for i, m in enumerate(msgs): ...
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log messages from each proc and print on demand
[ "log", "messages", "from", "each", "proc", "and", "print", "on", "demand" ]
[ "\"\"\" log messages from each proc and print on demand \"\"\"" ]
[ { "param": "self", "type": null }, { "param": "msg", "type": null }, { "param": "flush", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "msg", "type": null, "docstring": null, "docstring_tokens": []...
df855507450f584965b0b409cb442ecdd1beb6a9
Hipparcus/Python-Learning
aula6_pratica_gabarito.py
[ "MIT" ]
Python
insere_palavra_versaoAlunos
<not_specific>
def insere_palavra_versaoAlunos(frase, palavra, s): ''' Esta versao foi brilhantemente desenvolvida em sala pelos alunos: Eric Abreu e Camila Paredes & Mayara Miranda e Paola Ferreira Professor Kleber apenas renomeou as variaveis e colocou o str.join diretamente no return.''' if palavra...
Esta versao foi brilhantemente desenvolvida em sala pelos alunos: Eric Abreu e Camila Paredes & Mayara Miranda e Paola Ferreira Professor Kleber apenas renomeou as variaveis e colocou o str.join diretamente no return.
Esta versao foi brilhantemente desenvolvida em sala pelos alunos: Eric Abreu e Camila Paredes & Mayara Miranda e Paola Ferreira Professor Kleber apenas renomeou as variaveis e colocou o str.join diretamente no return.
[ "Esta", "versao", "foi", "brilhantemente", "desenvolvida", "em", "sala", "pelos", "alunos", ":", "Eric", "Abreu", "e", "Camila", "Paredes", "&", "Mayara", "Miranda", "e", "Paola", "Ferreira", "Professor", "Kleber", "apenas", "renomeou", "as", "variaveis", "e", ...
def insere_palavra_versaoAlunos(frase, palavra, s): if palavra in frase: return str.replace(frase, palavra, str.upper(palavra)) else: lista_string = str.split(frase) list.insert(lista_string, s, palavra) return str.join(" ", lista_string)
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Esta versao foi brilhantemente desenvolvida em sala pelos alunos: Eric Abreu e Camila Paredes & Mayara Miranda e Paola Ferreira Professor Kleber apenas renomeou as variaveis e colocou o str.join diretamente no return.
[ "Esta", "versao", "foi", "brilhantemente", "desenvolvida", "em", "sala", "pelos", "alunos", ":", "Eric", "Abreu", "e", "Camila", "Paredes", "&", "Mayara", "Miranda", "e", "Paola", "Ferreira", "Professor", "Kleber", "apenas", "renomeou", "as", "variaveis", "e", ...
[ "''' Esta versao foi brilhantemente desenvolvida em sala pelos alunos:\n\n\t Eric Abreu e Camila Paredes\t& Mayara Miranda e Paola Ferreira\n Professor Kleber apenas renomeou as variaveis e colocou o str.join\n diretamente no return.'''" ]
[ { "param": "frase", "type": null }, { "param": "palavra", "type": null }, { "param": "s", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "frase", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "palavra", "type": null, "docstring": null, "docstring_tokens...
1f7e555bab08030b1214848a1e828a991c67624a
thalespaiva/attack-on-binary-pkp
attack.py
[ "MIT" ]
Python
keygen_weak
<not_specific>
def keygen_weak(self, attack_param_w, attack_param_la, max_tries_for_A_gen=MAX_TRIES_FOR_A_GEN, max_tries_for_V_gen=MAX_TRIES_FOR_V_GEN): """ This function generates a random key pair for binary PKP that can be attacked with...
This function generates a random key pair for binary PKP that can be attacked with parameters (w, la) = (attack_param_w, attack_param_la). That is, it returns a triple (A, Vsec, Vpub) such that A is an m by n binary matrix V and Vsec are two n by l binary matrices ...
This function generates a random key pair for binary PKP that can be attacked with parameters (w, la) = (attack_param_w, attack_param_la). That is, it returns a triple (A, Vsec, Vpub) such that A is an m by n binary matrix V and Vsec are two n by l binary matrices A*Vsec = 0 Vsec has no two equal rows (security require...
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def keygen_weak(self, attack_param_w, attack_param_la, max_tries_for_A_gen=MAX_TRIES_FOR_A_GEN, max_tries_for_V_gen=MAX_TRIES_FOR_V_GEN): assert(attack_param_la <= self.m) for _try_A in range(max_tries_for_A_gen): ...
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This function generates a random key pair for binary PKP that can be attacked with parameters (w, la) = (attack_param_w, attack_param_la).
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[ "\"\"\"\n This function generates a random key pair for binary PKP that can be attacked with parameters\n (w, la) = (attack_param_w, attack_param_la). That is, it returns a triple (A, Vsec, Vpub) such\n that\n A is an m by n binary matrix\n V and Vsec are two n by l binary...
[ { "param": "self", "type": null }, { "param": "attack_param_w", "type": null }, { "param": "attack_param_la", "type": null }, { "param": "max_tries_for_A_gen", "type": null }, { "param": "max_tries_for_V_gen", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "attack_param_w", "type": null, "docstring": "Attack parameter w", ...
1f7e555bab08030b1214848a1e828a991c67624a
thalespaiva/attack-on-binary-pkp
attack.py
[ "MIT" ]
Python
keygen
<not_specific>
def keygen(self, max_tries_for_A_gen=MAX_TRIES_FOR_A_GEN, max_tries_for_V_gen=MAX_TRIES_FOR_V_GEN): """ This function generates a random key pair for binary PKP. That is, it returns a triple (A, Vsec, Vpub) such that A is an m by n binary matrix ...
This function generates a random key pair for binary PKP. That is, it returns a triple (A, Vsec, Vpub) such that A is an m by n binary matrix V and Vsec are two n by l binary matrices A*Vsec = 0 Vsec has no two equal rows (security requirement for PKP) ...
This function generates a random key pair for binary PKP. That is, it returns a triple (A, Vsec, Vpub) such that A is an m by n binary matrix V and Vsec are two n by l binary matrices A*Vsec = 0 Vsec has no two equal rows (security requirement for PKP) Vpub is a permutation of Vsec
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def keygen(self, max_tries_for_A_gen=MAX_TRIES_FOR_A_GEN, max_tries_for_V_gen=MAX_TRIES_FOR_V_GEN): for _try_A in range(max_tries_for_A_gen): A = random_matrix(GF(2), self.m, self.n) if A.rank() < self.m: continue try: ...
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This function generates a random key pair for binary PKP.
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[ "\"\"\"\n This function generates a random key pair for binary PKP.\n That is, it returns a triple (A, Vsec, Vpub) such that\n A is an m by n binary matrix\n V and Vsec are two n by l binary matrices\n A*Vsec = 0\n Vsec has no two equal rows (security requir...
[ { "param": "self", "type": null }, { "param": "max_tries_for_A_gen", "type": null }, { "param": "max_tries_for_V_gen", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "max_tries_for_A_gen", "type": null, "docstring": null, "docst...
1f7e555bab08030b1214848a1e828a991c67624a
thalespaiva/attack-on-binary-pkp
attack.py
[ "MIT" ]
Python
phase1_find_low_weight_keywords
<not_specific>
def phase1_find_low_weight_keywords(self): ''' Returns the low weight sets of vectors LWSA and LWSK, corresponding to vectors in the rowspace of A and K, respectively, where K is the left kernel matrix of Vpub. ''' LWSA = self.find_low_weight_codewords_in_rowspace(self.A) ...
Returns the low weight sets of vectors LWSA and LWSK, corresponding to vectors in the rowspace of A and K, respectively, where K is the left kernel matrix of Vpub.
Returns the low weight sets of vectors LWSA and LWSK, corresponding to vectors in the rowspace of A and K, respectively, where K is the left kernel matrix of Vpub.
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def phase1_find_low_weight_keywords(self): LWSA = self.find_low_weight_codewords_in_rowspace(self.A) K = self.Vpub.left_kernel().basis_matrix() LWSK = self.find_low_weight_codewords_in_rowspace(K) return LWSA, LWSK
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Returns the low weight sets of vectors LWSA and LWSK, corresponding to vectors in the rowspace of A and K, respectively, where K is the left kernel matrix of Vpub.
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[ "'''\n Returns the low weight sets of vectors LWSA and LWSK, corresponding to vectors in the\n rowspace of A and K, respectively, where K is the left kernel matrix of Vpub.\n '''" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
8b5162cd6d4f1729a6225a4f775ebe79fc8eaa4b
fossabot/pixie-1
src/api/python/tests/helpers/test_utils.py
[ "Apache-2.0" ]
Python
end
vpb.ExecuteScriptResponse
def end(self) -> vpb.ExecuteScriptResponse: """ Sends an end stream message. """ return vpb.ExecuteScriptResponse( status=_ok(), data=vpb.QueryData(batch=self.row_batch( [[]] * len(self.relation.columns), eos=True, eow=True ...
Sends an end stream message.
Sends an end stream message.
[ "Sends", "an", "end", "stream", "message", "." ]
def end(self) -> vpb.ExecuteScriptResponse: return vpb.ExecuteScriptResponse( status=_ok(), data=vpb.QueryData(batch=self.row_batch( [[]] * len(self.relation.columns), eos=True, eow=True )), )
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Sends an end stream message.
[ "Sends", "an", "end", "stream", "message", "." ]
[ "\"\"\" Sends an end stream message. \"\"\"" ]
[ { "param": "self", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "self", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
0fac94eb82a38312711d6f405ecc724adafea987
fossabot/pixie-1
src/cloud/dnsmgr/scripts/renew_certs_for_domain.py
[ "Apache-2.0" ]
Python
cert_fname_to_domain
<not_specific>
def cert_fname_to_domain(fname): ''' Converts a filename for a certificate to the domain that the cert satisfies. ''' assert fname.startswith('_'), '{} must start with "_"'.format(fname) assert fname.find( '_', 1) == -1, '{} must not contain "_" other than in the first character'.format(...
Converts a filename for a certificate to the domain that the cert satisfies.
Converts a filename for a certificate to the domain that the cert satisfies.
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def cert_fname_to_domain(fname): assert fname.startswith('_'), '{} must start with "_"'.format(fname) assert fname.find( '_', 1) == -1, '{} must not contain "_" other than in the first character'.format(fname) return fname.replace('_', '*')
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Converts a filename for a certificate to the domain that the cert satisfies.
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[ "'''\n Converts a filename for a certificate to the domain that the cert\n satisfies.\n '''" ]
[ { "param": "fname", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "fname", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
0fac94eb82a38312711d6f405ecc724adafea987
fossabot/pixie-1
src/cloud/dnsmgr/scripts/renew_certs_for_domain.py
[ "Apache-2.0" ]
Python
renew_lego_cert
<not_specific>
def renew_lego_cert(domain, out_dir, email, lego, num_tries): ''' Function that wraps the lego cmd to renew the lego certificate. ''' cmd = '{lego} --email="{email}" --domains="{domain}" ' \ '--dns="gcloud" --path="{out_dir}" -k rsa4096 -a run'.format( lego=lego, email=em...
Function that wraps the lego cmd to renew the lego certificate.
Function that wraps the lego cmd to renew the lego certificate.
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def renew_lego_cert(domain, out_dir, email, lego, num_tries): cmd = '{lego} --email="{email}" --domains="{domain}" ' \ '--dns="gcloud" --path="{out_dir}" -k rsa4096 -a run'.format( lego=lego, email=email, domain=domain, out_dir=out_dir, ) print('\n...
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Function that wraps the lego cmd to renew the lego certificate.
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[ "'''\n Function that wraps the lego cmd to renew the lego certificate.\n '''" ]
[ { "param": "domain", "type": null }, { "param": "out_dir", "type": null }, { "param": "email", "type": null }, { "param": "lego", "type": null }, { "param": "num_tries", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "domain", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "out_dir", "type": null, "docstring": null, "docstring_token...
0fac94eb82a38312711d6f405ecc724adafea987
fossabot/pixie-1
src/cloud/dnsmgr/scripts/renew_certs_for_domain.py
[ "Apache-2.0" ]
Python
renew_all_certs
<not_specific>
def renew_all_certs(domains, out_dir, email, lego, num_tries): ''' Renews all of the certificates for the domains passed in. ''' failed_certs = [] for d in list(domains): domains.remove(d) res = renew_lego_cert(d, out_dir, email, lego, num_tries) # TODO(philkuz) this doesn't ...
Renews all of the certificates for the domains passed in.
Renews all of the certificates for the domains passed in.
[ "Renews", "all", "of", "the", "certificates", "for", "the", "domains", "passed", "in", "." ]
def renew_all_certs(domains, out_dir, email, lego, num_tries): failed_certs = [] for d in list(domains): domains.remove(d) res = renew_lego_cert(d, out_dir, email, lego, num_tries) if not res: failed_certs.append(d) return failed_certs
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Renews all of the certificates for the domains passed in.
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[ "'''\n Renews all of the certificates for the domains passed in.\n '''", "# TODO(philkuz) this doesn't actually append the right things.", "# Was able to successfully update creds without problems" ]
[ { "param": "domains", "type": null }, { "param": "out_dir", "type": null }, { "param": "email", "type": null }, { "param": "lego", "type": null }, { "param": "num_tries", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "domains", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "out_dir", "type": null, "docstring": null, "docstring_toke...
ab1c3a8a9ef0e422442fb23d3e3a54926530eb33
nchammas/spark-pr-dashboard
sparkprs/jira_api.py
[ "Apache-2.0" ]
Python
link_issue_to_pr
<not_specific>
def link_issue_to_pr(issue, pr): """ Create a link in JIRA to a pull request and add a comment linking to the PR. This method is idempotent; the links will only be created if they do not already exist. """ jira_client = get_jira_client() url = pr.pr_json['html_url'] title = "[Github] Pull R...
Create a link in JIRA to a pull request and add a comment linking to the PR. This method is idempotent; the links will only be created if they do not already exist.
Create a link in JIRA to a pull request and add a comment linking to the PR. This method is idempotent; the links will only be created if they do not already exist.
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def link_issue_to_pr(issue, pr): jira_client = get_jira_client() url = pr.pr_json['html_url'] title = "[Github] Pull Request #%s (%s)" % (pr.number, pr.user) existing_links = map(lambda l: l.raw['object']['url'], jira_client.remote_links(issue)) if url in existing_links: return icon = {"...
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Create a link in JIRA to a pull request and add a comment linking to the PR.
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[ "\"\"\"\n Create a link in JIRA to a pull request and add a comment linking to the PR.\n\n This method is idempotent; the links will only be created if they do not already exist.\n \"\"\"" ]
[ { "param": "issue", "type": null }, { "param": "pr", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "issue", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "pr", "type": null, "docstring": null, "docstring_tokens": []...
baa50fcd581d551ab7470872350f03463f19bd42
nchammas/spark-pr-dashboard
sparkprs/utils.py
[ "Apache-2.0" ]
Python
parse_pr_title
<not_specific>
def parse_pr_title(pr_title): """ Parse a pull request title to identify JIRAs, categories, and the remainder of the title. >>> parse_pr_title("[SPARK-975] [core] Visual debugger of stages and callstacks") {'jiras': [975], 'title': 'Visual debugger of stages and callstacks', 'metadata': ''} >>>...
Parse a pull request title to identify JIRAs, categories, and the remainder of the title. >>> parse_pr_title("[SPARK-975] [core] Visual debugger of stages and callstacks") {'jiras': [975], 'title': 'Visual debugger of stages and callstacks', 'metadata': ''} >>> parse_pr_title("Documentation update...
Parse a pull request title to identify JIRAs, categories, and the remainder of the title.
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def parse_pr_title(pr_title): (metadata, rest) = re.match(r"""((?: # The metadata consists of either: (?:\[[^\]]*\]\s*) # Tags enclosed in brackets, like [CORE] |(?:SPARK-\d+\s*) # JIRA issues, like SPARK-957 ...
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Parse a pull request title to identify JIRAs, categories, and the remainder of the title.
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[ "\"\"\"\n Parse a pull request title to identify JIRAs, categories, and the\n remainder of the title.\n\n >>> parse_pr_title(\"[SPARK-975] [core] Visual debugger of stages and callstacks\")\n {'jiras': [975], 'title': 'Visual debugger of stages and callstacks', 'metadata': ''}\n >>> parse_pr_title(\"...
[ { "param": "pr_title", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "pr_title", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
bec819268525b63005ea1603ef2a6bca456c6918
nekhaly/network-analyzer
tests/test_analyzer.py
[ "MIT" ]
Python
assert_similar_strings
null
def assert_similar_strings(string_1, string_2): """Assert that the string are equal to the exception of white spaces and new lines""" assert string_1.translate({ord(" "): None, ord("\n"): None}) == string_2.translate( {ord(" "): None, ord("\n"): None} )
Assert that the string are equal to the exception of white spaces and new lines
Assert that the string are equal to the exception of white spaces and new lines
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def assert_similar_strings(string_1, string_2): assert string_1.translate({ord(" "): None, ord("\n"): None}) == string_2.translate( {ord(" "): None, ord("\n"): None} )
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Assert that the string are equal to the exception of white spaces and new lines
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[ "\"\"\"Assert that the string are equal to the exception of white spaces and new lines\"\"\"" ]
[ { "param": "string_1", "type": null }, { "param": "string_2", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "string_1", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "string_2", "type": null, "docstring": null, "docstring_to...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
extract_dsm
null
def extract_dsm(lidar_fn, out_fn, stat, in_srs="EPSG:2180", out_srs="EPSG:2178"): """the first assumption of the function was to calculate the results for both DSM and DTM (only in trees areas) from lidar data. Due to complication with DTM (the height was overstated by about 5m) right now it's used only ...
the first assumption of the function was to calculate the results for both DSM and DTM (only in trees areas) from lidar data. Due to complication with DTM (the height was overstated by about 5m) right now it's used only for calculating DSM. The "stat" parameter was originally needed to differentiate ...
the first assumption of the function was to calculate the results for both DSM and DTM (only in trees areas) from lidar data. Due to complication with DTM (the height was overstated by about 5m) right now it's used only for calculating DSM. The "stat" parameter was originally needed to differentiate between both layers
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def extract_dsm(lidar_fn, out_fn, stat, in_srs="EPSG:2180", out_srs="EPSG:2178"): start = time.time() elevation = "DTM" if stat == "min" else "DSM" print("{} extracting...".format(elevation)) pdal_json = { "pipeline": [ "{}".format(lidar_fn), { "type": "fi...
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the first assumption of the function was to calculate the results for both DSM and DTM (only in trees areas) from lidar data.
[ "the", "first", "assumption", "of", "the", "function", "was", "to", "calculate", "the", "results", "for", "both", "DSM", "and", "DTM", "(", "only", "in", "trees", "areas", ")", "from", "lidar", "data", "." ]
[ "\"\"\"the first assumption of the function was to calculate the results for both\r\n DSM and DTM (only in trees areas) from lidar data. Due to complication with\r\n DTM (the height was overstated by about 5m) right now it's used only for\r\n calculating DSM. The \"stat\" parameter was originally needed to...
[ { "param": "lidar_fn", "type": null }, { "param": "out_fn", "type": null }, { "param": "stat", "type": null }, { "param": "in_srs", "type": null }, { "param": "out_srs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "lidar_fn", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "out_fn", "type": null, "docstring": null, "docstring_toke...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
match_size_dtm
<not_specific>
def match_size_dtm(dsm_fn, tmp_dtm_fn, dtm_fn): """Filling no data value in calculated DTM raster, also changing raster sizes matching it to DSM raster size (PDAL creates DSM a little bit smaller)""" driver_tiff = gdal.GetDriverByName("GTiff") dsm_ds = gdal.Open(dsm_fn) tmp_dtm_ds = gdal.Open...
Filling no data value in calculated DTM raster, also changing raster sizes matching it to DSM raster size (PDAL creates DSM a little bit smaller)
Filling no data value in calculated DTM raster, also changing raster sizes matching it to DSM raster size (PDAL creates DSM a little bit smaller)
[ "Filling", "no", "data", "value", "in", "calculated", "DTM", "raster", "also", "changing", "raster", "sizes", "matching", "it", "to", "DSM", "raster", "size", "(", "PDAL", "creates", "DSM", "a", "little", "bit", "smaller", ")" ]
def match_size_dtm(dsm_fn, tmp_dtm_fn, dtm_fn): driver_tiff = gdal.GetDriverByName("GTiff") dsm_ds = gdal.Open(dsm_fn) tmp_dtm_ds = gdal.Open(tmp_dtm_fn) cols = dsm_ds.RasterXSize rows = dsm_ds.RasterYSize temp_dtm_data = tmp_dtm_ds.GetRasterBand(1).ReadAsArray() dtm_data = np.zeros((rows, c...
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Filling no data value in calculated DTM raster, also changing raster sizes matching it to DSM raster size (PDAL creates DSM a little bit smaller)
[ "Filling", "no", "data", "value", "in", "calculated", "DTM", "raster", "also", "changing", "raster", "sizes", "matching", "it", "to", "DSM", "raster", "size", "(", "PDAL", "creates", "DSM", "a", "little", "bit", "smaller", ")" ]
[ "\"\"\"Filling no data value in calculated DTM raster, also changing raster sizes matching it to\r\n DSM raster size (PDAL creates DSM a little bit smaller)\"\"\"", "# dtm_data = temp_dtm_ds.GetRasterBand(1).ReadAsArray()\r", "# FILLING NO DATA VALUE IN GROUND RASTER\r", "# inplace, filling gaps under buil...
[ { "param": "dsm_fn", "type": null }, { "param": "tmp_dtm_fn", "type": null }, { "param": "dtm_fn", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "dsm_fn", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "tmp_dtm_fn", "type": null, "docstring": null, "docstring_to...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
chm_calculate
null
def chm_calculate(tmp_dtm_fn, dtm_fn, dsm_fn, chm_fn): """Calculating CHM from DSM and DTM""" start = time.time() print("CHM calculating...") # LOADING DRIVER driver_tiff = gdal.GetDriverByName("GTiff") # OPEN DATASET & READ DATA temp_dtm_ds = gdal.Open(tmp_dtm_fn, 1) # GA_Update ...
Calculating CHM from DSM and DTM
Calculating CHM from DSM and DTM
[ "Calculating", "CHM", "from", "DSM", "and", "DTM" ]
def chm_calculate(tmp_dtm_fn, dtm_fn, dsm_fn, chm_fn): start = time.time() print("CHM calculating...") driver_tiff = gdal.GetDriverByName("GTiff") temp_dtm_ds = gdal.Open(tmp_dtm_fn, 1) dsm_ds = gdal.Open(dsm_fn, 1) dsm_data = dsm_ds.GetRasterBand(1).ReadAsArray() dtm_ds = gdal.Open(dtm_fn,...
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Calculating CHM from DSM and DTM
[ "Calculating", "CHM", "from", "DSM", "and", "DTM" ]
[ "\"\"\"Calculating CHM from DSM and DTM\"\"\"", "# LOADING DRIVER\r", "# OPEN DATASET & READ DATA\r", "# GA_Update\r", "# PDAL CREATES DIFFERENT XSIZE, YSIZE RASTERS FOR DEM AND DSM\r", "# saving data from ground raster to new one with changed x,y sizes\r", "# cols = dsm_ds.RasterXSize if dsm_ds.RasterX...
[ { "param": "tmp_dtm_fn", "type": null }, { "param": "dtm_fn", "type": null }, { "param": "dsm_fn", "type": null }, { "param": "chm_fn", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "tmp_dtm_fn", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "dtm_fn", "type": null, "docstring": null, "docstring_to...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
chm_segmentation
<not_specific>
def chm_segmentation(dsm_fn): """CHM ata filtering, masking and watershed segmentation. Idea taken from https://www.neonscience.org/resources/learning-hub/tutorials/calc-biomass-py""" driver = gdal.GetDriverByName("GTiff") dsm_ds = gdal.Open(dsm_fn) dsm_ds.GetRasterBand(1).SetNoDataValue(0) ...
CHM ata filtering, masking and watershed segmentation. Idea taken from https://www.neonscience.org/resources/learning-hub/tutorials/calc-biomass-py
CHM ata filtering, masking and watershed segmentation.
[ "CHM", "ata", "filtering", "masking", "and", "watershed", "segmentation", "." ]
def chm_segmentation(dsm_fn): driver = gdal.GetDriverByName("GTiff") dsm_ds = gdal.Open(dsm_fn) dsm_ds.GetRasterBand(1).SetNoDataValue(0) chm_array = dsm_ds.GetRasterBand(1).ReadAsArray().astype(np.float32) start = time.time() print("Watershed segmentation...") chm_array_smooth = ndi.gaussia...
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CHM ata filtering, masking and watershed segmentation.
[ "CHM", "ata", "filtering", "masking", "and", "watershed", "segmentation", "." ]
[ "\"\"\"CHM ata filtering, masking and watershed segmentation.\r\n Idea taken from https://www.neonscience.org/resources/learning-hub/tutorials/calc-biomass-py\"\"\"", "# APPLYING GAUSSIAN FILTER TO REMOVE WRONG POINTS\r", "# CALCULATE LOCAL MAXIMUM POINTS\r", "# CREATE MASK TO MATCH INPUT ARRAY SIZE\r", ...
[ { "param": "dsm_fn", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "dsm_fn", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
create_layer
<not_specific>
def create_layer(ds, epsg=2178): """Create shapefile layer. Used for temporary and segments shp""" srs = osr.SpatialReference() srs.ImportFromEPSG(epsg) layer = ds.CreateLayer('segments.shp', srs, ogr.wkbPolygon) layer.CreateField(ogr.FieldDefn('SEG_NB', ogr.OFTInteger)) layer.CreateFiel...
Create shapefile layer. Used for temporary and segments shp
Create shapefile layer. Used for temporary and segments shp
[ "Create", "shapefile", "layer", ".", "Used", "for", "temporary", "and", "segments", "shp" ]
def create_layer(ds, epsg=2178): srs = osr.SpatialReference() srs.ImportFromEPSG(epsg) layer = ds.CreateLayer('segments.shp', srs, ogr.wkbPolygon) layer.CreateField(ogr.FieldDefn('SEG_NB', ogr.OFTInteger)) layer.CreateField(ogr.FieldDefn('MAX_H', ogr.OFTReal)) return layer
[ "def", "create_layer", "(", "ds", ",", "epsg", "=", "2178", ")", ":", "srs", "=", "osr", ".", "SpatialReference", "(", ")", "srs", ".", "ImportFromEPSG", "(", "epsg", ")", "layer", "=", "ds", ".", "CreateLayer", "(", "'segments.shp'", ",", "srs", ",", ...
Create shapefile layer.
[ "Create", "shapefile", "layer", "." ]
[ "\"\"\"Create shapefile layer. Used for temporary and segments shp\"\"\"" ]
[ { "param": "ds", "type": null }, { "param": "epsg", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "ds", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "epsg", "type": null, "docstring": null, "docstring_tokens": [],...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
copy_attributes
null
def copy_attributes(tmp_lyr, segments_lyr): """Copying attributes form temporary shp layer to segments""" for feat in tmp_lyr: if feat.GetField(0) != -9999: geom = feat.GetGeometryRef() fld_name = feat.GetFieldDefnRef(0).GetName() out_feat = ogr.Feature(segmen...
Copying attributes form temporary shp layer to segments
Copying attributes form temporary shp layer to segments
[ "Copying", "attributes", "form", "temporary", "shp", "layer", "to", "segments" ]
def copy_attributes(tmp_lyr, segments_lyr): for feat in tmp_lyr: if feat.GetField(0) != -9999: geom = feat.GetGeometryRef() fld_name = feat.GetFieldDefnRef(0).GetName() out_feat = ogr.Feature(segments_lyr.GetLayerDefn()) out_feat.SetGeometry(geom) ...
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Copying attributes form temporary shp layer to segments
[ "Copying", "attributes", "form", "temporary", "shp", "layer", "to", "segments" ]
[ "\"\"\"Copying attributes form temporary shp layer to segments\"\"\"" ]
[ { "param": "tmp_lyr", "type": null }, { "param": "segments_lyr", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "tmp_lyr", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "segments_lyr", "type": null, "docstring": null, "docstring...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
bounding_box_to_offsets
<not_specific>
def bounding_box_to_offsets(bbox, transform): """Calculating boxboundary offsets for each segment""" col1 = int((bbox[0] - transform[0]) / transform[1]) col2 = int((bbox[1] - transform[0]) / transform[1]) + 1 row1 = int((bbox[3] - transform[3]) / transform[5]) row2 = int((bbox[2] - transform[3]...
Calculating boxboundary offsets for each segment
Calculating boxboundary offsets for each segment
[ "Calculating", "boxboundary", "offsets", "for", "each", "segment" ]
def bounding_box_to_offsets(bbox, transform): col1 = int((bbox[0] - transform[0]) / transform[1]) col2 = int((bbox[1] - transform[0]) / transform[1]) + 1 row1 = int((bbox[3] - transform[3]) / transform[5]) row2 = int((bbox[2] - transform[3]) / transform[5]) + 1 return [row1, row2, col1, col2]
[ "def", "bounding_box_to_offsets", "(", "bbox", ",", "transform", ")", ":", "col1", "=", "int", "(", "(", "bbox", "[", "0", "]", "-", "transform", "[", "0", "]", ")", "/", "transform", "[", "1", "]", ")", "col2", "=", "int", "(", "(", "bbox", "[",...
Calculating boxboundary offsets for each segment
[ "Calculating", "boxboundary", "offsets", "for", "each", "segment" ]
[ "\"\"\"Calculating boxboundary offsets for each segment\"\"\"" ]
[ { "param": "bbox", "type": null }, { "param": "transform", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "bbox", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "transform", "type": null, "docstring": null, "docstring_token...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
offsets_transform
<not_specific>
def offsets_transform(row_offset, col_offset, transform): """Calculating new geotransform for each segment boxboundary""" new_geotransform = [ transform[0] + (col_offset * transform[1]), transform[1], 0.0, transform[3] + (row_offset * transform[5]), 0.0, t...
Calculating new geotransform for each segment boxboundary
Calculating new geotransform for each segment boxboundary
[ "Calculating", "new", "geotransform", "for", "each", "segment", "boxboundary" ]
def offsets_transform(row_offset, col_offset, transform): new_geotransform = [ transform[0] + (col_offset * transform[1]), transform[1], 0.0, transform[3] + (row_offset * transform[5]), 0.0, transform[5]] return new_geotransform
[ "def", "offsets_transform", "(", "row_offset", ",", "col_offset", ",", "transform", ")", ":", "new_geotransform", "=", "[", "transform", "[", "0", "]", "+", "(", "col_offset", "*", "transform", "[", "1", "]", ")", ",", "transform", "[", "1", "]", ",", ...
Calculating new geotransform for each segment boxboundary
[ "Calculating", "new", "geotransform", "for", "each", "segment", "boxboundary" ]
[ "\"\"\"Calculating new geotransform for each segment boxboundary\"\"\"" ]
[ { "param": "row_offset", "type": null }, { "param": "col_offset", "type": null }, { "param": "transform", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "row_offset", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "col_offset", "type": null, "docstring": null, "docstrin...
eb8a660c5d7ad36a5caf9624518c0f078a0ac531
slowiklukasz/chm_pdal
chm_zd.py
[ "MIT" ]
Python
calculate_zstats
<not_specific>
def calculate_zstats(fid, min, max, mean, median, sd, sum, count): """Calculating basic statistic, determining maximum height in segment""" names = ["id", "min", "max", "mean", "median", "sd", "sum", "count"] feat_stats = {names[0]: fid, names[1]: min, names[2]: max,...
Calculating basic statistic, determining maximum height in segment
Calculating basic statistic, determining maximum height in segment
[ "Calculating", "basic", "statistic", "determining", "maximum", "height", "in", "segment" ]
def calculate_zstats(fid, min, max, mean, median, sd, sum, count): names = ["id", "min", "max", "mean", "median", "sd", "sum", "count"] feat_stats = {names[0]: fid, names[1]: min, names[2]: max, names[3]: mean, names[4]: median, ...
[ "def", "calculate_zstats", "(", "fid", ",", "min", ",", "max", ",", "mean", ",", "median", ",", "sd", ",", "sum", ",", "count", ")", ":", "names", "=", "[", "\"id\"", ",", "\"min\"", ",", "\"max\"", ",", "\"mean\"", ",", "\"median\"", ",", "\"sd\"", ...
Calculating basic statistic, determining maximum height in segment
[ "Calculating", "basic", "statistic", "determining", "maximum", "height", "in", "segment" ]
[ "\"\"\"Calculating basic statistic, determining maximum height in segment\"\"\"" ]
[ { "param": "fid", "type": null }, { "param": "min", "type": null }, { "param": "max", "type": null }, { "param": "mean", "type": null }, { "param": "median", "type": null }, { "param": "sd", "type": null }, { "param": "sum", "type": nul...
{ "returns": [], "raises": [], "params": [ { "identifier": "fid", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "min", "type": null, "docstring": null, "docstring_tokens": [],...