Author: xiangcaohello



12.26 USCIS 收到

3.20号左右:Request for more evidence。 要求signature,之前我的签字要么是用了蓝色笔,要么是签字太大,超出范围了。

4.1号把RFE packet寄回, 顺便把跳槽后renewed的H1b/H4 notice 加进去了。

4.5 USCIS 收到

4.10 更新: Card in production

4.19更新: Card mailed out



从去年开始到现在,所有的积蓄在股市亏光了,玩短期option进入了赌博模式,前后大概亏了12万美金。去年亏了7万,当时想收手了,说要把钱交给老婆管,复习了几个月刷题跳槽成功,今年年初老婆在复习考NY Bar,没空管理家里财务,我又鬼使神差的重新进去想回本,结果一把我又亏了5万。银行里现在一共也只有1万不到。





San Francisco City Hall Wedding Guide

How to Get Married at San Francisco City Hall

You can either get the marriage license on the same day or beforehand. you should at least get the marriage license 30 minutes before the ceremony.

you can get the marriage license in other city hall within California.

Santa Clara City Hall:


Everything about Python Dict

This is a  great post I found on stackoverflow.


Here is everything about Python dicts that I was able to put together (probably more than anyone would like to know; but the answer is comprehensive).

  • Python dictionaries are implemented as hash tables.
  • Hash tables must allow for hash collisions i.e. even if two distinct keys have the same hash value, the table’s implementation must have a strategy to insert and retrieve the key and value pairs unambiguously.
  • Python dict uses open addressing to resolve hash collisions (explained below) (see dictobject.c:296-297).
  • Python hash table is just a contiguous block of memory (sort of like an array, so you can do an O(1) lookup by index).
  • Each slot in the table can store one and only one entry. This is important.
  • Each entry in the table actually a combination of the three values: < hash, key, value >. This is implemented as a C struct (see dictobject.h:51-56).
  • The figure below is a logical representation of a Python hash table. In the figure below, 0, 1, ..., i, ... on the left are indices of the slots in the hash table (they are just for illustrative purposes and are not stored along with the table obviously!).
    # Logical model of Python Hash table
    0| <hash|key|value>|
    1|      ...        |
    .|      ...        |
    i|      ...        |
    .|      ...        |
    n|      ...        |
  • When a new dict is initialized it starts with 8 slots. (see dictobject.h:49)
  • When adding entries to the table, we start with some slot, i, that is based on the hash of the key. CPython initially uses i = hash(key) & mask (where mask = PyDictMINSIZE - 1, but that’s not really important). Just note that the initial slot, i, that is checked depends on the hash of the key.
  • If that slot is empty, the entry is added to the slot (by entry, I mean, <hash|key|value>). But what if that slot is occupied!? Most likely because another entry has the same hash (hash collision!)
  • If the slot is occupied, CPython (and even PyPy) compares the the hash AND the key (by compare I mean == comparison not the is comparison) of the entry in the slot against the key of the current entry to be inserted (dictobject.c:337,344-345). If both match, then it thinks the entry already exists, gives up and moves on to the next entry to be inserted. If either hash or the key don’t match, it starts probing.
  • Probing just means it searches the slots by slot to find an empty slot. Technically we could just go one by one, i+1, i+2, ... and use the first available one (that’s linear probing). But for reasons explained beautifully in the comments (see dictobject.c:33-126), CPython uses random probing. In random probing, the next slot is picked in a pseudo random order. The entry is added to the first empty slot. For this discussion, the actual algorithm used to pick the next slot is not really important (see dictobject.c:33-126 for the algorithm for probing). What is important is that the slots are probed until first empty slot is found.
  • The same thing happens for lookups, just starts with the initial slot i (where i depends on the hash of the key). If the hash and the key both don’t match the entry in the slot, it starts probing, until it finds a slot with a match. If all slots are exhausted, it reports a fail.
  • BTW, the dict will be resized if it is two-thirds full. This avoids slowing down lookups. (see dictobject.h:64-65)

NOTE: I did the research on Python Dict implementation in response to my own question about how multiple entries in a dict can have same hash values. I posted a slightly edited version of the response here because all the research is very relevant for this question as well.